WO2023115482A1 - Rhythm analysis and decision-making method and apparatus for defibrillator, and storage medium - Google Patents

Abstract

A rhythm analysis and decision-making method and apparatus for a defibrillator, and a storage medium. The method comprises: obtaining an electrocardiosignal of a target object in the process of performing cardiopulmonary resuscitation on the target object; dividing the electrocardiosignal into a plurality of analysis areas on a time sequence, and determining a state corresponding to each analysis area; for each analysis area, determining a fragment rhythm analysis mode on the basis of the corresponding state, and performing rhythm analysis to obtain a fragment rhythm state of the analysis area; performing long-term rhythm analysis on the basis of the fragment rhythm states of the plurality of analysis areas within a first preset time period from a rhythm decision-making moment to obtain a long-term rhythm state feature; performing short-term rhythm analysis on the basis of the fragment rhythm state of at least one analysis area within a second preset time period from the rhythm decision-making moment to obtain a short-term rhythm state feature; and determining and outputting a rhythm decision on the basis of the long-term rhythm state feature and the short-term rhythm state feature. The present application can obtain a more reliable rhythm decision.

Description

Rhythm analysis and decision-making method, device and storage medium for defibrillator

manual

technical field

The present application relates to the technical field of defibrillators, and more particularly relates to a rhythm analysis and decision-making method, device and storage medium for defibrillators.

Background technique

The defibrillator is a medical device that uses a strong pulse current to pass through the heart to eliminate arrhythmia and restore it to sinus rhythm. It is an essential first aid equipment in the operating room. Defibrillation is one of the most important steps in performing CPR. Cardiopulmonary Resuscitation (CPR) compressions need to be combined with electrocardiograph (ECG) signals for rhythm analysis. In CPR compressions, reliable rhythm analysis mainly depends on reliable segmental rhythm analysis and reliable multi-time segmental rhythm status synthesis. decision making.

At present, rhythm comprehensive decision-making methods in the process of CPR mainly focus on two aspects: one is to use short-term rhythm analysis state to comprehensively output rhythm decision-making, and the other is to use long-term rhythm analysis state to comprehensively output rhythm decision-making. However, comprehensive decision-making only using the short-term rhythm analysis state is easily affected by local disturbances, leading to rhythm misjudgment; only relying on the long-term rhythm analysis state for comprehensive decision-making reflects a long-term stable rhythm state and is not sensitive to local rhythm fluctuations. Insensitivity, rhythm switching response is not timely.

Contents of the invention

In one aspect of the present application, a rhythm analysis and decision-making method for a defibrillator is provided. The method includes: acquiring an ECG signal of the target object during cardiopulmonary resuscitation of the target object; The time series is divided into multiple analysis areas, and the state corresponding to each analysis area is determined, and the state includes first aid treatment state and/or filtering state; for each of the analysis areas, based on the state corresponding to the analysis area, the determination The segmental rhythm analysis mode of the analysis area, and perform rhythm analysis on the analysis area based on the segmental rhythm analysis mode, so as to obtain the segmental rhythm state of the analysis area; Perform long-term rhythm analysis on the segmental rhythm states of a plurality of the analysis regions to obtain long-term rhythm state characteristics; perform short-term rhythm analysis based on the segmental rhythm states of at least one of the analysis regions within the second preset time period from the rhythm decision-making moment Rhythm analysis, obtaining short-term rhythm state characteristics, wherein the first preset time period is greater than the second preset time period; determining rhythm decisions based on the long-term rhythm state characteristics and the short-term rhythm state characteristics, and output the rhythm decision.

Another aspect of the present application provides a rhythm analysis and decision-making method for a defibrillator, the method comprising: acquiring a reference signal during cardiopulmonary resuscitation of a target object and the original ECG signal of the target object; The reference signal performs compression detection to obtain the time-domain compression event marker of the reference signal; the instantaneous compression interval is determined based on the time-domain compression event marker, and the original ECG signal is processed based on the instantaneous compression interval performing filtering to obtain filtered ECG signals; dividing the ECG signals of the target object into multiple analysis areas in time series, and performing rhythm analysis on each analysis area to obtain fragments of each analysis area Rhythm state, wherein, the ECG signal of the target object only includes the filtered ECG signal, or, the ECG signal of the target object includes the original ECG signal and the filtered ECG signal ; Long-term rhythm analysis is performed based on the segmental rhythm states of a plurality of the analysis regions in the first preset time period from the rhythm decision-making moment to obtain the long-term rhythm state characteristics; Short-term rhythm analysis is performed on the segmental rhythm state of at least one of the analysis regions to obtain short-term rhythm state characteristics, wherein the first preset time period is greater than the second preset time period; based on the long-term rhythm state A feature and the short-term rhythm state feature determine a rhythm decision, and output the rhythm decision.

In yet another aspect of the present application, a rhythm analysis and decision-making device is provided, the device includes a memory and a processor, the memory stores a computer program run by the processor, and the computer program is executed by the processor The rhythm analysis and decision-making method described above for the defibrillator is implemented at runtime.

In yet another aspect of the present application, a storage medium is provided, on which a computer program is stored, and the computer program executes the above rhythm analysis and decision-making method for a defibrillator when running.

According to the rhythm analysis and decision-making method and device for the defibrillator according to the embodiment of the present application, the rhythm decision is determined by combining the long-term rhythm state characteristics and the short-term rhythm state characteristics, which can not only avoid local interference, but also avoid local rhythm fluctuations. Sensitive, resulting in more reliable rhythm decisions. Moreover, since different rhythm analysis modes are selected according to ECG signals in different states, the accuracy of the rhythm analysis result, that is, the segmental rhythm state, can be further improved, thereby further improving the reliability of rhythm decision-making.

Description of drawings

Fig. 1 shows a schematic flowchart of a rhythm analysis and decision-making method for a defibrillator according to an embodiment of the present application.

Fig. 2 shows an exemplary flowchart of a rhythm analysis and decision-making method for a defibrillator according to an embodiment of the present application.

Fig. 3 shows a process diagram of a rhythm analysis and decision-making method for a defibrillator according to an embodiment of the present application.

Fig. 4 shows a schematic flowchart of a rhythm analysis and decision-making method for a defibrillator according to another embodiment of the present application.

Fig. 5 shows an exemplary flowchart of a rhythm analysis and decision-making method for a defibrillator according to another embodiment of the present application.

Fig. 6 shows an exemplary flowchart of a rhythm analysis and decision-making method for a defibrillator according to yet another embodiment of the present application.

Fig. 7 shows a schematic block diagram of a rhythm analysis and decision-making device for a defibrillator according to an embodiment of the present application.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described here. Based on the embodiments of the present application described in the present application, all other embodiments obtained by those skilled in the art without creative effort shall fall within the protection scope of the present application.

In the following description, numerous specific details are given in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced without one or more of these details. In other examples, some technical features known in the art are not described in order to avoid confusion with the present application.

It should be understood that the present application can be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the/the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the terms "consists of" and/or "comprising", when used in this specification, identify the presence of stated features, integers, steps, operations, elements and/or components, but do not exclude the presence of one or more other Presence or addition of features, integers, steps, operations, elements, parts and/or groups. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

In order to thoroughly understand the present application, detailed steps and detailed structures will be provided in the following description, so as to explain the technical solutions proposed in the present application. The preferred embodiments of the present application are described in detail as follows, however, the present application may have other implementations besides these detailed descriptions.

First, a rhythm analysis and decision-making method for a defibrillator according to an embodiment of the present application will be described with reference to FIG. 1 . Fig. 1 shows a schematic flowchart of a rhythm analysis and decision-making method 100 for a defibrillator according to an embodiment of the present application. As shown in FIG. 1 , a rhythm analysis and decision-making method 100 for a defibrillator may include the following steps:

In step S110, the electrocardiographic signal of the target object is acquired during the process of performing cardiopulmonary resuscitation on the target object.

In step S120, the electrocardiographic signal is divided into a plurality of analysis areas in time series, and a state corresponding to each analysis area is determined, and the state includes an emergency treatment state and/or a filtering state.

In step S130, for each of the analysis areas, the segmental rhythm analysis mode of the analysis area is determined based on the state corresponding to the analysis area, and the rhythm analysis is performed on the analysis area based on the segmental rhythm analysis mode to obtain Segmental rhythm status of the analyzed region.

In step S140, long-term rhythm analysis is performed based on the segmental rhythm states of the plurality of analysis regions within the first preset time period from the rhythm decision-making moment to obtain long-term rhythm state characteristics.

In step S150, short-term rhythm analysis is performed based on the segmental rhythm state of at least one of the analysis regions within a second preset time period from the rhythm decision-making moment to obtain short-term rhythm state characteristics, wherein the first preset time period greater than the second preset time period.

In step S160, a rhythm decision is determined based on the long-term rhythm state feature and the short-term rhythm state feature, and the rhythm decision is output.

In the embodiment of the present application, after the ECG signal of the target object is divided into a plurality of analysis areas in time series during the cardiopulmonary resuscitation process of the target object, the state corresponding to each analysis area (emergency treatment state and/or filter state), for the analysis areas with different states, different segmental rhythm analysis modes are used for rhythm analysis, and the segmental rhythm status of each analysis area is obtained. Finally, the long-term rhythm status analysis and the long-short rhythm State analysis, combined with long-term rhythm state characteristics and short-term rhythm state characteristics to determine rhythm decision-making, can not only avoid local interference, but also avoid insensitivity to local rhythm fluctuations, so that more reliable rhythm decision-making can be obtained. Moreover, since different rhythm analysis modes are selected according to ECG signals in different states, the accuracy of the rhythm analysis result, that is, the segmental rhythm state, can be further improved, thereby further improving the reliability of rhythm decision-making.

In the embodiment of the present application, the ECG signal of the target object acquired in step S110 may be the original ECG signal and/or the filtered ECG signal. Wherein, the filtered ECG signal is obtained based on reference signals related to chest compressions and original ECG signals during cardiopulmonary resuscitation. Specifically, time-domain compression event detection and identification of compression events can be performed on reference signals (such as chest impedance signals collected by defibrillation transthoracic electrodes, blood oxygen signals, respiratory signals, and signals sensed by CPR sensors, etc.). The domain component assists in the detection of compression events, and then uses the adaptive filtering model to filter out the CPR interference of the original ECG signal waveform, realizes CPR chest compression detection and filtering, and obtains the filtered ECG signal.

In the embodiment of the present application, in step S120, the ECG signal is divided into multiple analysis areas in time series, and each analysis area can have the same time length; or, the time length of each analysis area can be changed as required . In addition, for two adjacent analysis regions, they can be continuous or discontinuous in time. In addition, for two adjacent analysis regions, they may partially overlap or not overlap in time.

In the embodiment of the present application, the status of each analysis area includes first aid treatment status and/or filtering status. Wherein, the first aid treatment state reflects the first aid operation state of the first aid personnel on the target object in the time period corresponding to the analysis area; the filtering state reflects whether the ECG signal in the time period corresponding to the analysis area needs to be filtered. Since different emergency operation states and whether the ECG signal needs to be filtered reflect whether the ECG signal is interfered by CPR in a period of time, and whether the ECG signal is interfered by CPR affects the rhythm analysis and decision-making, therefore, in this application In the embodiment, the state of each analysis area of the ECG signal is determined, and the appropriate (corresponding) segmental rhythm analysis mode is determined according to the state of each analysis area of the ECG signal, which is conducive to performing rhythm analysis on each analysis area more accurately , to obtain a more accurate segmental rhythm state, which is conducive to obtaining a more reliable rhythm decision.

In the embodiment of the present application, the first aid treatment state may further include a compression progress state (also referred to simply as a compression state), a compression transition state, and a compression pause state. The filtering status may further include a filtering status and a filtering status. In an embodiment of the present application, determining the segmental rhythm analysis mode of the analysis area based on the state corresponding to the analysis area may include: when the first aid treatment state of the analysis area is the pressing pause state and/or the filtering state of the analysis area is When no filtering state is required, determine that the segmental rhythm analysis mode of the analysis area is the non-interference mode; when the first aid treatment state of the analysis area is a pressing state or a pressing transition state, and the filtering state of the analysis area is a filtering state When , it is determined that the segmental rhythm analysis mode of the analysis region is an interference mode.

As mentioned above, different emergency operation states and whether ECG signals need to be filtered reflect whether ECG signals are interfered by CPR in a period of time, and whether ECG signals are interfered by CPR affects rhythm analysis and decision-making. Therefore, when it is determined that the first aid treatment state of an analysis area is the compression pause state, it indicates that the ECG signal is less interfered by CPR at this time, so the segmental rhythm analysis can be performed in the interference-free mode. Similarly, when it is determined that the filtering state of an analysis region is no filtering state, it indicates that the ECG signal is less disturbed by CPR at this time, so the segmental rhythm analysis can be performed in the interference-free mode. In the non-interference mode, both the original ECG signal and the filtered ECG signal can be used for rhythm analysis. Therefore, the non-interference rhythm strategy can be used for rhythm analysis based on the original ECG signal and/or the filtered ECG signal.

When it is determined that the first aid treatment status of an analysis area is the compression progress state or the compression transition state, it indicates that the ECG signal is greatly interfered by CPR at this time, so it is necessary to use the interference mode for segmental rhythm analysis. Similarly, when it is determined that the filtering state of an analysis area is a filtering state, it indicates that the ECG signal is greatly interfered by CPR at this time, and thus it is necessary to use the interference mode for segmental rhythm analysis. In the interference mode, since the ECG signal is interfered by CPR, the rhythm analysis can be performed based on the filtered ECG signal only; or, the original ECG signal and the filtered ECG signal can be combined with Rhythm analysis with interfering rhythm strategies.

In the embodiment of the present application, the emergency treatment state may also include an electric shock state, and the determination of the segmental rhythm analysis mode of the analysis area based on the state corresponding to the analysis area may also include: when the first aid treatment of the analysis area When the state is the electric shock state, it is determined that the segmental rhythm analysis mode of the analysis area is the initialization mode. In the electric shock state, a new round of compression cycle can be started by default, the rhythm analysis is initialized, and the first analysis after the electric shock is used as the initial state of the rhythm analysis, and the rhythm information before the electric shock is no longer considered. For example, the shock status can be sensed when the first responder presses the shock confirmation button.

In the embodiment of the present application, determining the state corresponding to each analysis area in step S120 may further include: acquiring reference signals related to chest compressions during cardiopulmonary resuscitation of the target object; Divide it into a plurality of sub-analysis areas, and determine the pressing situation corresponding to each sub-analysis area; for each analysis area of the ECG signal, based on the Press the condition to determine the first aid treatment status for each of said analyzed regions. In this embodiment, a reference signal (such as chest impedance signal, blood oxygen signal, respiratory signal, and signal sensed by the CPR sensor, etc. collected by the defibrillation transthoracic electrode) is obtained and divided into a plurality of sub-analysis areas, and one of the reference signals One or more sub-analysis areas correspond to one analysis area of the ECG signal. Therefore, according to the pressing situation of one or more sub-analysis areas corresponding to one analysis area, the emergency treatment status of the analysis area is determined.

In an example, the determining the first aid treatment status of each analysis area based on the pressing situation of one or more sub-analysis areas corresponding to the analysis area may include: When the corresponding pressing situation of each of the sub-analysis areas is that there is a pressing event, it is determined that the emergency treatment state of the analysis area is the pressing-in-progress state; When the pressing situation of some sub-analysis areas is that there is a pressing event, and the pressing situation of the remaining sub-analysis areas is that there is no pressing event, it is determined that the emergency treatment state of the analysis area is a pressing transition state; when each corresponding to the analysis area When there is no compression event in each of the sub-analysis areas, it is determined that the emergency treatment state of the analysis area is the compression pause state.

In an embodiment of the present application, the determination of the pressing situation corresponding to each sub-analysis area may include: performing time-domain analysis and/or frequency-domain analysis on the reference signal to obtain time-domain pressing features and/or frequency-domain analysis. Press feature: perform press detection on each sub-analysis area of the reference signal based on the time-domain press feature and/or the frequency-domain press feature, so as to determine whether there is a press event in each sub-analysis area. In this embodiment, it may be determined whether there is a pressing event in each sub-analysis area based on time-domain pressing features, frequency-domain pressing features, or a combination of time-domain and frequency-domain pressing features, so as to more accurately determine whether there is a pressing event in each sub-analysis area. press event.

The above is an example description of determining the first aid treatment status for each analysis region. The determination of the filter state for each analysis region is described below. In an embodiment of the present application, determining the state corresponding to each analysis area may include: acquiring a reference signal during cardiopulmonary resuscitation of the target object; dividing the reference signal into multiple sub-analysis areas in time series ; For each analysis area of the ECG signal, perform correlation analysis on the reference signal of one or more sub-analysis areas corresponding to the analysis area and the noise in the analysis area, according to the correlation Determine the filtering mode of the analysis area according to the result of the property analysis, and determine the filtering state of the analysis area according to the filtering mode of the analysis area.

In this embodiment, a reference signal (such as chest impedance signal, blood oxygen signal, respiratory signal, and signal sensed by the CPR sensor, etc. collected by the defibrillation transthoracic electrode) is obtained and divided into a plurality of sub-analysis areas, and one of the reference signals One or more sub-analysis areas correspond to an analysis area of the electrocardiogram signal, therefore, the correlation is performed according to the reference signal part corresponding to one or more sub-analysis areas corresponding to one analysis area and the noise condition of the analysis area Analysis, you can determine the filtering mode of the analysis area, so as to determine the filtering status of the analysis area. Exemplarily, when it is determined through correlation analysis that the ECG filtering mode of an analysis region is low-order filtering or high-order filtering, it may be determined that the filtering state of the analysis region is a required filtering state. When the correlation analysis determines that the ECG filtering mode of an analysis region is no filtering, it can be determined that the filtering state of the analysis region is no filtering.

In the embodiment of the present application, after determining the state of each analysis area and thus determining the segmental rhythm analysis mode of each analysis area, the segmental rhythm analysis is performed on each analysis area using the segmental rhythm analysis mode corresponding to each analysis area , so as to obtain the segmental rhythm status of each analysis area, as described in step S130. After that, according to the obtained segmental rhythm state, a long-term rhythm analysis and a short-term rhythm analysis are respectively performed in steps S140 and S150, which will be described in detail below.

In the embodiment of the present application, long-term rhythm analysis is performed based on segmental rhythm states of multiple analysis regions within a first preset time period from the rhythm decision-making moment to obtain long-term rhythm state characteristics. That is to say, the long-term rhythm state is obtained by performing rhythm analysis on the segmental rhythm states of multiple analysis areas, and the multiple analysis areas are multiple analysis areas within a first preset time period from the rhythm decision-making moment. Here, the first preset time period refers to the time period from the moment when the decision-making moment goes back to the first preset time to the rhythm decision-making moment, and the end point of this time period can be the rhythm decision-making moment, or Instead of a rhythmic decision moment, it can be a certain time away from that decision moment. In one example, the analysis duration of the long-term rhythm analysis is the time range of more than 10 seconds closest to the rhythm decision-making moment, but not exceeding the current compression cycle (generally 2 minutes or 3 minutes), including segmental rhythms of at least 5 analysis regions state. Usually, multiple segmental rhythm states within 2 minutes from the rhythm decision-making moment can be analyzed to calculate long-term rhythm state features. In this example, the above-mentioned first preset time period is equal to 2 minutes.

In the embodiment of the present application, the long-term rhythm state characteristics may include at least one of the following: the proportion of each segment rhythm state in the set of segment rhythm states of each of the multiple analysis regions; When the respective segmental rhythm states of the analysis areas are sorted by time, the proportion of the same segmental rhythm states for multiple consecutive times; in the corresponding state sets of the plurality of analysis areas, the time proportions of different states and/or different states The proportion of each fragment rhythm state; the weighted score of each fragment rhythm state in the collection of fragment rhythm states of multiple said analysis regions, wherein the weight of the fragment rhythm state of each said analysis region is The size depends on the time distance between the analysis area and the rhythm decision moment and/or the state corresponding to the analysis area.

In an embodiment of the present application, for an analysis area: the closer the analysis area is to the rhythm decision-making moment, the greater the weight assigned to the segmental rhythm state of the analysis area; and when the emergency treatment state of the analysis area is When the compression pause state or the filtering state is no filtering state, assign the first weight to the segmental rhythm state of the analysis area; when the first aid treatment state of the analysis area is a pressing state and the filtering state is a filtering state, all The segmental rhythm state of the analysis area is assigned a second weight, wherein the second weight is smaller than the first weight; when the emergency treatment state of the analysis area is a pressing transition state and the filtering state is a filtering state, the The segmental rhythm state of the analyzed region is assigned a third weight, wherein the third weight is less than the second weight.

In this embodiment, from the time dimension, the closer the rhythm decision-making moment is, the greater the weight assigned to the segmental rhythm state at the corresponding moment. From the perspective of signal reliability, if the first aid treatment status is the compression pause state or the filtering state is no filtering state, the ECG signal without CPR interference is analyzed at this time, and the segmental rhythm state analysis results are relatively reliable, and the segmental rhythm state at the corresponding time is given. Assign the first weight coefficient; if the state of first aid treatment is the state of pressing and the state of filtering is the state of needing filtering, at this time the analysis is the ECG signal interfered by CPR, the reliability of the segmental rhythm state analysis results is reduced, and the segmental rhythm state at the corresponding moment is given Assign a second weight coefficient (the second weight coefficient is less than the first weight coefficient); if the pressing state of the current analysis area is a pressing transition state and the filtering state is a state requiring filtering, the signals analyzed at this time include ECG signals interfered by CPR and without For ECG signals interfered by CPR, due to the influence of CPR filtering, there may be large differences in amplitude and shape between the ECG signals after CPR filtering and the ECG signals without CPR filtering in the compression transition region, and the analysis results of segmental rhythm status are unreliable. A third weight coefficient is assigned to the segmental rhythm state at the corresponding moment (the third weight coefficient is smaller than the second weight coefficient). In the embodiment of the present application, the rhythm state weight allocation criteria of the signal reliability dimension and the time dimension can be integrated, and the segmental rhythm states of different analysis areas can be assigned respective weights, and weighted combinations can be performed to obtain the weighted score of each rhythm state. value. If the emergency treatment state of the current analysis area is electric shock state, enter the initialization mode, initialize the long-term rhythm analysis, and start a new round of compression cycle by default. Preshock rhythm information is no longer considered.

In an embodiment of the present application, short-term rhythm analysis is performed based on the segmental rhythm state of at least one analysis region within a second preset time period from the rhythm decision-making moment to obtain short-term rhythm state characteristics. That is to say, the long-term rhythm state is obtained by performing rhythm analysis on the segmental rhythm state of at least one analysis area, and the at least one analysis area is at least one analysis area within the second preset time period from the rhythm decision-making moment. Here, the second preset time period refers to the time period from the decision-making moment back to the second preset time to the rhythm decision-making moment, and the end of the time period can be the rhythm decision-making moment, or Instead of a rhythmic decision moment, it can be a certain time away from that decision moment. In one example, the analysis duration of the short-term rhythm analysis is the time range within 10 seconds closest to the rhythm decision-making moment, and at least includes the segmental rhythm state of one analysis area. Usually, one or more segments of the rhythm state within 10 seconds from the rhythm decision-making moment can be analyzed to calculate short-term rhythm state characteristics. In this example, the aforementioned second preset time period is equal to 10 seconds.

In the embodiment of the present application, the characteristics of the short-term rhythm state may include at least one of the following: the proportion of each segment rhythm state in the set of segment rhythm states of each of the multiple analysis regions; When the respective segmental rhythm states of the analysis areas are sorted by time, the proportion of the same segmental rhythm states for multiple consecutive times; in the corresponding state sets of the plurality of analysis areas, the time proportions of different states and/or different states The proportion of each fragment rhythm state; the weighted score of each fragment rhythm state in the collection of fragment rhythm states of multiple said analysis regions, wherein the weight of the fragment rhythm state of each said analysis region is The size depends on the time distance between the analysis area and the rhythm decision-making moment and/or the state corresponding to the analysis area; among the analysis areas whose state is the pressing pause state or the no-filtering state, the analysis area closest to the rhythm decision-making moment is the same as the rhythm decision-making moment. The temporal distance of the moment and the percentage of its fragment rhythmic state.

In the embodiment of the present application, similar to the long-term rhythm analysis described above, in the short-term rhythm analysis, for an analysis area: the closer the analysis area is to the rhythm decision-making moment, the segmental rhythm of the analysis area The greater the weight assigned to the state; and when the first aid treatment state of the analysis area is a press pause state or the filtering state is a state without filtering, assign the first weight to the segmental rhythm state of the analysis area; when the analysis area When the state of emergency treatment is pressing and the state of filtering is the state of requiring filtering, a second weight is assigned to the segmental rhythm state of the analysis area, wherein the second weight is smaller than the first weight; when the first aid of the analysis area When the processing state is a pressing transition state and the filtering state is a filtering state, a third weight is assigned to the segmental rhythm state of the analysis region, wherein the third weight is smaller than the second weight.

In this embodiment, from the time dimension, the closer the rhythm decision-making moment is, the greater the weight assigned to the segmental rhythm state at the corresponding moment. From the perspective of signal reliability, if the first aid treatment status is the compression pause state or the filtering state is no filtering state, the ECG signal without CPR interference is analyzed at this time, and the segmental rhythm state analysis results are relatively reliable, and the segmental rhythm state at the corresponding time is given. Assign the first weight coefficient; if the state of first aid treatment is the state of pressing and the state of filtering is the state of needing filtering, at this time the analysis is the ECG signal interfered by CPR, the reliability of the segmental rhythm state analysis results is reduced, and the segmental rhythm state at the corresponding moment is given Assign a second weight coefficient (the second weight coefficient is less than the first weight coefficient); if the pressing state of the current analysis area is a pressing transition state and the filtering state is a state requiring filtering, the signals analyzed at this time include ECG signals interfered by CPR and without For ECG signals interfered by CPR, due to the influence of CPR filtering, there may be large differences in amplitude and shape between the ECG signals after CPR filtering and the ECG signals without CPR filtering in the compression transition region, and the analysis results of segmental rhythm status are unreliable. A third weight coefficient is assigned to the segmental rhythm state at the corresponding moment (the third weight coefficient is smaller than the second weight coefficient). In the embodiment of the present application, the rhythm state weight allocation criteria of the signal reliability dimension and the time dimension can be integrated, and the segmental rhythm states of different analysis areas can be assigned respective weights, and weighted combinations can be performed to obtain the weighted score of each rhythm state. value. If the first aid treatment status of the current analysis area is the electric shock state, enter the initialization mode, initialize the short-term rhythm analysis, start a new round of compression cycle by default, and no longer consider the rhythm information before the electric shock.

After the long-term rhythm state feature and the short-term rhythm state feature are obtained, the rhythm decision is determined by combining the two. In an embodiment of the present application, the determining the rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics may include: when the long-term rhythm state characteristics and the short-term rhythm state characteristics are respectively When the distribution of the reflected segmental rhythm state is consistent, the rhythm decision is determined according to the distribution of the segmental rhythm state reflected by the long-term rhythm state feature or the short-term rhythm state feature; when the long-term rhythm state feature and the When the distributions of the segmental rhythm states reflected by the short-term rhythm state characteristics are inconsistent, the rhythm decision is determined according to the more reliable of the two distributions, or, according to the segmental rhythm state embodied by the short-term rhythm state characteristics The distribution determines the rhythm decision, or the output does not determine the rhythm decision. Among them, as described above in terms of the dimension of signal reliability, the analysis regions of different states correspond to different ECG signals, and the reliability of the analysis results of the segmental rhythm states is also different. Therefore, the reliability of the distribution of the segmental rhythm state represented by the long-term rhythm state feature and the short-term rhythm state feature is related to the state of the analysis area used in the long-term rhythm analysis and the short-term rhythm analysis respectively.

In this embodiment, the decision whether to defibrillate is made according to the judgment criterion established by the distribution characteristics of the long-term rhythm state and the short-term rhythm state. If the distribution characteristics of the long-term rhythm state are consistent with the distribution characteristics of the short-term rhythm state, the output can be defibrillated according to the rhythm state represented by the distribution characteristics; if the distribution characteristics of the long-term rhythm state and the distribution characteristics of the short-term rhythm state If the distribution characteristics of the long-term rhythm state are inconsistent with the distribution characteristics of the short-term rhythm state, and the two None of the distribution features are reliable, and the output is uncertain rhythm decision. For example, if the weighted score feature in the long-term rhythm analysis indicates that the long-term rhythm status is shockable, the weighted score is large, and there are no compression pauses or periods without filtering in the short-term rhythm analysis, and the shockable The proportion of rhythm state is relatively large, and the output can be defibrillated rhythm decision; if the characteristics of rhythm state percentage and weighted score in the long-term rhythm analysis have little distinction on whether the rhythm state can be defibrillated or not, but in the short-term rhythm analysis. The defibrillable rhythm state can also be output in the period of time when the compression is paused or does not need to be filtered, and the time period closest to the rhythm decision-making moment has a large proportion of the defibrillable rhythm state.

In another embodiment of the present application, the determining the rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics may include: for each segment rhythm state, according to the long-term rhythm state characteristics The weighted combination of the feature corresponding to the segmental rhythm state and the short-term rhythmic state feature and the feature corresponding to the segmental rhythm state obtains the weighted score of the segmental rhythm state; according to each segment The weighted score of the rhythm status determines the rhythm decision. In this embodiment, according to the distribution characteristics of the long-term rhythm state and the short-term rhythm state, the weighted score obtained through weighted combination is used for rhythm decision-making, which can be expressed as the following formula:

RhythmScore=A*LongTimeScore+B*ShortTimeScore+C,

Among them, RhythmScore is a weighted score of a certain rhythm state. According to the weighted score of the rhythm state, it can be defibrillated, not defibrillated, or uncertain. LongTimeScore is a numerical value for measuring the long-term rhythm state distribution characteristics of a certain rhythm state, and may be a value for measuring the distribution characteristics of a long-term rhythm state or a combined value for measuring the distribution characteristics of multiple long-term rhythm states. For example, LongTimeScore can be the distribution value (such as percentage) of different rhythm states in the long-term rhythm state feature, or it can be converted from the distribution values of different rhythm states in the long-term rhythm state feature (such as weighted score normalized value). Wherein, the combination method may be, for example, performing normalization, averaging, weighted average, and other calculations on various long-duration and long-duration rhythm state distribution characteristics to obtain combined values and the like. ShortTimeScore is a numerical value for measuring the distribution characteristics of a short-term rhythm state of a certain rhythm state, and may be a value for measuring the distribution characteristics of a short-term rhythm state or a combined value for measuring the distribution characteristics of multiple short-term rhythm states. A, B, and C are weight coefficients obtained from regression analysis.

After the rhythm decision is determined, the rhythm decision can be output, for example, at the end of the compression cycle, and according to the rhythm decision result, the emergency personnel are instructed to perform first aid treatment. If the rhythm decision output can be defibrillated rhythm decision, instruct the emergency personnel to give electric shock, if the rhythm decision output is not defibrillation rhythm decision, instruct the emergency personnel to continue to press, if the rhythm decision output is not sure rhythm decision, instruct the emergency personnel to suspend compression, for no compression ECG signal under interference for rhythm confirmation.

The rhythm analysis and decision-making method 100 for a defibrillator according to the embodiment of the present application is described above by taking the fixed CPR operation mode as an example. Cardiopulmonary resuscitation, giving rhythm decisions after the compression cycle, instructing first responders to perform first aid treatment. The method 100 can also be used in the continuous CPR operation mode, wherein the continuous CPR operation mode means that there is no fixed compression cycle during the cardiopulmonary resuscitation process of the emergency personnel, and the rhythm decision is continuously made during the CPR process. Once the rhythm decision is made, the defibrillation rhythm is output. Immediately instruct the emergency personnel to perform electric shock treatment, or the emergency personnel initiate a rhythm analysis request, give rhythm decisions, and instruct the emergency personnel to perform first aid treatment.

In an embodiment for the continuous CPR mode of operation, in one example, the analysis duration of the long-term rhythm analysis may be in the time range of more than 10 seconds to the nearest rhythm decision moment, but not more than 3 minutes, including at least 5 analyses. Segmental rhythm status of the region; the analysis duration of short-term rhythm analysis is within 10 seconds from the moment of rhythm decision-making, including the segmental rhythm status of at least one analysis region, and rhythm decision-making is continued during the CPR process. If the analysis time or the number of analysis regions before the rhythm decision-making moment does not meet the analysis conditions of long-term rhythm analysis, the long-term rhythm state remains the default initial state (uncertain rhythm), and the long-term default initial rhythm state and short-term rhythm state are combined in rhythm decision-making. Rhythm state characteristics, using the rhythm decision-making strategy, output defibrillation, non-shockable or uncertain rhythm decision. Rhythm decision-making strategies at this time include but are not limited to the following methods: directly output uncertain rhythm decision-making; determine whether to defibrillate according to the judgment criteria formulated by the characteristics of the short-term rhythm state. If measured from the time dimension and signal reliability dimension, short-term If the short-term rhythm state is unreliable, the output is uncertain rhythm decision (for example, if there is a pause in compression or no The period of time for filtering, and the proportion of the defibrillable rhythm state in the time period closest to the rhythm decision-making moment is relatively large, and the defibrillable rhythm decision is output); according to the characteristics of the long-term default initial rhythm state and short-term rhythm state, after weighted combination The obtained weighted score is used for rhythm decision-making, which can be expressed as the following formula:

RhythmScore=A*LongTimeScore+B*ShortTimeScore+C,

Among them, RhythmScore is the weighted score of a certain rhythm state, according to the weighted score of the rhythm state, the decision of defibrillation, non-defibrillation or uncertain rhythm is made; LongTimeScore is the value to measure the long-term default initial rhythm state; ShortTimeScore is the value to measure a certain The value of the short-term rhythm state distribution characteristics of a rhythm state can be a value that measures the distribution characteristics of a short-term rhythm state or a combined value that measures the distribution characteristics of multiple short-term rhythm states; A, B, and C are obtained by regression analysis weight factor.

According to the result of the rhythm decision, the emergency personnel are instructed to perform first aid treatment. When the emergency personnel do not actively initiate a rhythm analysis request, once the rhythm decision outputs a defibrillable rhythm, the emergency personnel are immediately instructed to give an electric shock. If the rhythm decision outputs other rhythms, the emergency personnel are not instructed or instructed to continue compressions. When the emergency personnel initiate a rhythm analysis request and give a rhythm decision, if the rhythm decision outputs a defibrillable rhythm, immediately instruct the emergency personnel to give an electric shock; if the rhythm decision outputs a defibrillable rhythm, instruct the emergency personnel to continue pressing; if the rhythm decision output is uncertain Rhythm, which instructs the rescuer to pause compressions and perform rhythm confirmation on the ECG signal without compression interference.

The above exemplarily shows the rhythm analysis and decision-making method 100 for the defibrillator according to the embodiment of the present application. In order to better understand the method, FIG. 2 shows a rhythm analysis and decision-making method according to the embodiment of the application. Exemplary flow chart (wherein it mainly shows each link and its trend in the whole process), and Fig. 3 shows a schematic diagram of the process of rhythm analysis and decision-making according to the embodiment of the present application (wherein it mainly shows various signals, State and rhythm state analysis considerations, weight distribution, etc.), you can better understand the content described above according to Figure 2 and Figure 3, and will not repeat them here.

In general, the rhythm analysis and decision-making method 100 for a defibrillator according to the embodiment of the present application not only considers the short-term rhythm state before the rhythm decision-making moment, but also considers the long-term rhythm state before the rhythm decision-making moment. The rhythm state in the cycle is generally relatively stable. By paying attention to the rhythm state in the CPR compression cycle for a long time before the rhythm decision-making moment, the stable rhythm state in the CPR compression cycle can be obtained. At the same time, in order to respond to local rhythm fluctuations in time, pay attention to the rhythm in the CPR compression cycle The rhythm state in a short period of time before the decision-making moment, the short-term instantaneous rhythm state is obtained, the long-term stable rhythm state and the short-term instantaneous rhythm state are integrated, the rhythm decision is made, and the emergency personnel are instructed to perform first aid treatment.

Based on the above description, the rhythm analysis and decision-making method 100 for a defibrillator according to the embodiment of the present application combines long-term rhythm state features and short-term rhythm state features to determine rhythm decisions, which can avoid local interference and avoid Insensitivity to local rhythm fluctuations enables more reliable rhythm decisions. Moreover, since different rhythm analysis modes are selected according to ECG signals in different states, the accuracy of the rhythm analysis result, that is, the segmental rhythm state, can be further improved, thereby further improving the reliability of rhythm decision-making.

A rhythm analysis and decision-making method 400 for a defibrillator according to another embodiment of the present application will be described below with reference to FIG. 4 . As shown in FIG. 4, a rhythm analysis and decision-making method 400 for a defibrillator may include the following steps:

In step S410, the reference signal during cardiopulmonary resuscitation of the target object and the original ECG signal of the target object are acquired.

In step S420, press detection is performed on the reference signal to obtain a time-domain press event marker of the reference signal.

In step S430, the instantaneous compression interval is determined based on the time-domain compression event marker, and the original electrocardiographic signal is filtered based on the instantaneous compression interval to obtain a filtered electrocardiographic signal.

In step S440, the ECG signal of the target object is divided into multiple analysis areas in time series, and rhythm analysis is performed on each analysis area to obtain the segmental rhythm state of each analysis area, wherein the target The ECG signal of the subject only includes the filtered ECG signal, or, the ECG signal of the target object includes the original ECG signal and the filtered ECG signal.

In step S450, long-term rhythm analysis is performed based on the segmental rhythm states of the plurality of analysis regions within the first preset time period from the rhythm decision-making moment to obtain long-term rhythm state characteristics.

In step S460, short-term rhythm analysis is performed based on the segmental rhythm state of at least one of the analysis regions within a second preset time period from the rhythm decision-making moment to obtain short-term rhythm state characteristics, wherein the first preset time period greater than the second preset time period.

In step S470, a rhythm decision is determined based on the long-term rhythm state feature and the short-term rhythm state feature, and the rhythm decision is output.

In this embodiment, first, the time-domain compression event marker is obtained based on the compression detection of the reference signal, and the instantaneous compression interval can be determined based on the time-domain compression event marker, so that the original ECG signal is filtered based on the instantaneous compression interval to obtain the filtered subsequent ECG signal. Similar to the embodiment described above, in this embodiment, the ECG signal of the target object during cardiopulmonary resuscitation (wherein, the ECG signal of the target object may only include the filtered The electrocardiographic signal, or the electrocardiographic signal of the target object may include the original electrocardiographic signal and the filtered electrocardiographic signal) are divided into a plurality of analysis areas in time series, and according to the segmental rhythm of each analysis area Long-term rhythm state analysis and short-term rhythm state analysis are carried out according to the state, and rhythm decision-making is determined by combining the characteristics of long-term rhythm state and short-term rhythm state, which can not only avoid local interference, but also avoid insensitivity to local rhythm fluctuations, so that more For reliable rhythm decisions. The difference is that in this embodiment, it is not necessary to determine the state corresponding to each analysis area, but directly perform rhythm analysis on each analysis area to obtain the segmental rhythm state of each analysis area. Therefore, this embodiment is similar to the above-mentioned embodiments, except that some steps are omitted. For the sake of brevity, only the operations in this embodiment are outlined here, and the details of these operations can be referred to the contents of the foregoing embodiments.

In an embodiment of the present application, performing time-domain press detection on the reference signal at step S420 to obtain a time-domain press event marker of the reference signal may include: performing time-domain analysis on the reference signal and/or Frequency-domain analysis to obtain time-domain pressing features and/or frequency-domain pressing features; performing pressing detection on the reference signal based on the time-domain pressing features and/or the frequency-domain pressing features to obtain the reference signal Time-domain compression event markers. In this embodiment, pressing detection may be performed based on time-domain features, frequency-domain features or a combination of the two, so as to obtain the time-domain pressing event marker of the reference signal.

In the embodiments of the present application, similar to the foregoing embodiments, the long-term rhythm status characteristics and the short-term rhythm status characteristics obtained in step S450 and step S460 may each include at least one of the following: In the collection of the respective fragment rhythm states of a plurality of said analysis areas, the proportion of each fragment rhythm state; when the respective fragment rhythm states of a plurality of said analysis regions are sorted by time, the proportion of the same fragment rhythm state for multiple consecutive times ratio; in the respective corresponding state sets of a plurality of said analysis areas, the time ratio of different states and/or the proportion of each segment rhythm state in different states; the respective segment rhythm states of a plurality of said analysis areas In the set of , the weighted score of each fragment rhythm state, wherein the weight of the fragment rhythm state of each analysis area depends on the time distance between the analysis area and the rhythm decision moment and/or the corresponding analysis area status.

In the embodiment of the present application, similar to the above-mentioned embodiments, determining the rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics in step S470 may include: when the long-term rhythm state When the characteristics and the distribution of the segmental rhythm state embodied by the short-term rhythm state feature are consistent, the rhythm decision is determined according to the distribution of the segmental rhythm state embodied by the long-term rhythm state feature or the short-term rhythm state feature; when When the distribution of the segmental rhythm state represented by the long-term rhythm state feature and the short-term rhythm state feature are inconsistent, the rhythm decision is determined according to the more reliable one of the two distributions, or, according to the short-term rhythm state The distribution of the segmental rhythmic state embodied by the rhythmic state feature determines the rhythmic decision, or outputs an indeterminate rhythmic decision. Alternatively, determining the rhythm decision based on the long-term rhythm state features and the short-term rhythm state features in step S470 may include: for each segmental rhythm state, A weighted combination of the corresponding features and the features corresponding to the short-term rhythm state and the segmental rhythm state to obtain the weighted score of the segmental rhythm state; determine according to the weighted score of each segmental rhythm state Rhythm decision.

In the embodiment of the present application, similar to the embodiment described above, the method 400 can be applied to the fixed cardiopulmonary resuscitation operation mode and the continuous cardiopulmonary resuscitation operation mode. When applied to the continuous cardiopulmonary resuscitation operation mode, if the rhythm decision time before When the analysis area is not enough for the long-term rhythm analysis, the long-term rhythm analysis is not performed, and the rhythm decision is determined according to the preset long-term default initial rhythm state and the characteristics of the short-term rhythm state.

The above exemplarily shows the rhythm analysis and decision-making method 400 for the defibrillator according to the embodiment of the present application. In order to better understand the method, FIG. 5 shows an example of the rhythm analysis and decision-making according to this embodiment A general flow chart (which mainly shows each link in the whole process and its direction), the content of the method 400 described above can be better understood according to the flow chart, and will not be repeated here. In addition, in another embodiment of the present application, the above-mentioned method 400 can omit steps S410 to S430, and start to execute directly from step S440. At this time, the electrocardiogram signal in step S440 can refer to the electrocardiogram after band-pass filtering of conventional ECG. The signal may also refer to the ECG signal filtered by CPR. In this embodiment, the electrocardiographic signal can be analyzed as a compression reference signal, as shown in the exemplary flow chart of FIG. 6 , which can be used as a variant of the method 400 .

Based on the above description, the rhythm analysis and decision-making method 400 for a defibrillator according to the embodiment of the present application and its deformation scheme combine long-term rhythm state characteristics and short-term rhythm state characteristics to determine rhythm decision-making, which can avoid local interference, It can also avoid insensitivity to local rhythm fluctuations, so that more reliable rhythm decisions can be obtained.

The rhythm analysis and decision-making method for the defibrillator according to the embodiment of the present application has been exemplarily described above. A rhythm analysis and decision-making device for a defibrillator according to another aspect of the present application is described below with reference to FIG. 7 . Fig. 7 shows a schematic block diagram of a rhythm analysis and decision-making device 700 for a defibrillator according to an embodiment of the present application. As shown in FIG. 7 , the rhythm analysis and decision-making device 700 for a defibrillator may include a memory 710 and a processor 720, the memory 710 is stored with a computer program run by the processor 720, and the computer program is executed by the processor 720 The rhythm analysis and decision-making method for the defibrillator according to the embodiment of the present application described above is executed during runtime. In a further embodiment of the present application, the rhythm analysis and decision-making device 700 for a defibrillator may also include a signal acquisition component 730, which may be used to acquire ECG signals and/or Reference signals related to chest compressions are sent to the processor 720, so that it can execute the rhythm analysis and decision-making method for the defibrillator according to the embodiment of the present application. In the embodiment of the present application, the above-mentioned device 700 may be a defibrillator. Those skilled in the art can understand the structure and operation of each component of the rhythm analysis and decision-making apparatus 700 for defibrillators according to the embodiment of the present application in combination with the foregoing descriptions, and for the sake of brevity, details are not repeated here.

In addition, according to the embodiment of the present application, there is also provided a storage medium, on which program instructions are stored, and when the program instructions are executed by a computer or a processor, they are used to execute the rhythm for the defibrillator according to the embodiment of the present application. The corresponding steps in the analysis and decision-making method. The storage medium may include, for example, a memory card of a smart phone, a storage unit of a tablet computer, a hard disk of a personal computer, a read only memory (ROM), an erasable programmable read only memory (EPROM), a portable compact disk read only memory (CD), etc. -ROM), USB memory, or any combination of the above storage media. The computer readable storage medium can be any combination of one or more computer readable storage medium.

In addition, according to an embodiment of the present application, a computer program is also provided, and the computer program may be stored in a cloud or a local storage medium. When the computer program is run by a computer or a processor, it is used to execute the corresponding steps of the rhythm analysis and decision-making method for a defibrillator in the embodiment of the present application.

Based on the above description, according to the embodiment of the present application, the rhythm analysis and decision-making method and device for defibrillator combine long-term rhythm state characteristics and short-term rhythm state characteristics to determine rhythm decision-making, which can avoid local interference and avoid Insensitivity to local rhythm fluctuations enables more reliable rhythm decisions. Moreover, according to the rhythm analysis and decision-making method and device for a defibrillator according to the embodiment of the present application, different rhythm analysis modes can be selected according to ECG signals in different states, which can further improve the accuracy of the rhythm analysis results, that is, the segmental rhythm state, Thereby further improving the reliability of rhythm decision-making.

Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above-described example embodiments are exemplary only, and are not intended to limit the scope of the application thereto. Various changes and modifications can be made therein by those of ordinary skill in the art without departing from the scope and spirit of the application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. A skilled artisan may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated. to another device, or some features may be ignored, or not implemented.

In the description provided herein, numerous specific details are set forth. However, it is understood that the embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Similarly, it should be understood that in the description of the exemplary embodiments of the application, in order to streamline the application and to facilitate understanding of one or more of the various inventive aspects, various features of the application are sometimes grouped together into a single embodiment, figure , or in its description. This method of application, however, is not to be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as the corresponding claims reflect, the inventive point lies in that the corresponding technical problem may be solved by using less than all features of a single disclosed embodiment. Thus, the claims following this Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this application.

It will be appreciated by those skilled in the art that all features disclosed in this specification (including accompanying claims, abstract and drawings) and all features of any method or apparatus so disclosed may be used in any combination, except where the features are mutually exclusive. process or unit. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

In addition, those skilled in the art will understand that although some embodiments herein include some features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the application And form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

The various component embodiments of the present application may be realized in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules according to the embodiments of the present application. The present application can also be implemented as an apparatus program (for example, a computer program and a computer program product) for performing a part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

The above is only the specific implementation of the application or the description of the specific implementation. The scope of protection of the application is not limited thereto. Any skilled person familiar with the technical field can easily think of changes within the technical scope disclosed in this application. Or replacement, should be covered within the protection scope of this application. The protection scope of the present application should be based on the protection scope of the claims.

Claims (26)

A rhythm analysis and decision-making method for a defibrillator, characterized in that the method comprises: Obtaining the ECG signal of the target object during the cardiopulmonary resuscitation process of the target object; Divide the ECG signal into multiple analysis areas in time series, and determine the state corresponding to each analysis area, the state includes first aid treatment state and/or filtering state; For each of the analysis areas, determine the segmental rhythm analysis mode of the analysis area based on the state corresponding to the analysis area, and perform rhythm analysis on the analysis area based on the segmental rhythm analysis mode to obtain the analysis area The segmental rhythmic state of Performing long-term rhythm analysis based on segmental rhythm states of a plurality of analysis regions within a first preset time period from the rhythm decision-making moment to obtain long-term rhythm state characteristics; Short-term rhythm analysis is performed based on the segmental rhythm state of at least one of the analysis regions within a second preset time period from the rhythm decision moment to obtain short-term rhythm state characteristics, wherein the first preset time period is longer than the first preset time period 2. Preset time period; A rhythm decision is determined based on the long-term rhythm state feature and the short-term rhythm state feature, and the rhythm decision is output. The method according to claim 1, wherein the first aid treatment status includes a compression progress status, a compression transition status, and a compression suspension status, and the filtering status includes a filtering status and a filtering status, and based on the analysis, The state corresponding to the region determines the segmental rhythm analysis mode of the analysis region, including: When the first aid treatment state of the analysis area is a pressing pause state and/or the filtering state of the analysis area is a no-filtering state, determine that the segmental rhythm analysis mode of the analysis area is a non-interference mode; When the first aid treatment state of the analysis area is the compression progress state or the compression transition state, and the filter state of the analysis area is the filtering state, it is determined that the segmental rhythm analysis mode of the analysis area is the interference mode. The method according to claim 2, wherein the emergency treatment state further includes an electric shock state, and determining the segmental rhythm analysis mode of the analysis region based on the state corresponding to the analysis region further includes: When the emergency treatment state of the analysis area is the electric shock state, it is determined that the segmental rhythm analysis mode of the analysis area is the initialization mode. The method according to claim 2, wherein the electrocardiographic signal includes an original electrocardiographic signal and a filtered electrocardiographic signal, and in the interference-free mode, based on the original electrocardiographic signal and/or filtered After the ECG signal, use the non-interference rhythm strategy for rhythm analysis; in the interference mode, only based on the filtered ECG signal, or based on the original ECG signal and the filtered ECG signal, use the Interference rhythm strategy for rhythm analysis. The method according to claim 2, wherein said determining the state corresponding to each analysis area comprises: Obtaining reference signals related to chest compressions during cardiopulmonary resuscitation of the target object; Dividing the reference signal into multiple sub-analysis areas in time series, and determining the pressing situation corresponding to each sub-analysis area; For each analysis area of the electrocardiogram, based on the pressing situation of one or more sub-analysis areas corresponding to the analysis area, the emergency treatment status of each analysis area is determined. The method according to claim 5, wherein the determining the emergency treatment status of each of the analysis regions based on the pressing conditions of one or more of the sub-analysis regions corresponding to the analysis region includes : When the compression condition of each of the sub-analysis areas corresponding to the analysis area is a compression event, determine that the emergency treatment state of the analysis area is a compression progress state; When in the plurality of sub-analysis areas corresponding to the analysis area, the pressing status of some sub-analysis areas is that there is a pressing event, and the pressing status of the rest of the sub-analysis areas is that there is no pressing event, determine the status of the analysis area. The first aid treatment state is a pressing transition state; When the compression condition of each sub-analysis area corresponding to the analysis area is that there is no compression event, it is determined that the emergency treatment state of the analysis area is a compression suspension state. The method according to claim 5, wherein said determining the pressing situation corresponding to each sub-analysis area comprises: performing time-domain analysis and/or frequency-domain analysis on the reference signal to obtain time-domain compression features and/or frequency-domain compression features; Performing press detection on each sub-analysis area of the reference signal based on the time-domain press feature and/or the frequency-domain press feature, so as to determine whether there is a press event in each sub-analysis area. The method according to claim 2, wherein said determining the state corresponding to each analysis area comprises: Obtain a reference signal during cardiopulmonary resuscitation of the target object; dividing the reference signal into a plurality of sub-analysis areas in time series; For each analysis area of the ECG signal, a correlation analysis is performed on the reference signal of one or more sub-analysis areas corresponding to the analysis area and the noise of the analysis area, and according to the correlation The analysis result determines the filtering mode of the analysis area, and determines the filtering state of the analysis area according to the filtering mode of the analysis area. The method according to claim 2, wherein the long-term rhythm state characteristics and the short-term rhythm state characteristics include at least one of the following: The proportion of each segment rhythm state in the set of segment rhythm states of each of the plurality of analysis regions; When the segmental rhythm states of multiple analysis regions are sorted according to time, the proportion of the same segmental rhythm states for multiple consecutive times; In the corresponding state sets of each of the plurality of analysis regions, the time ratio of different states and/or the proportion of each segment rhythm state in different states; A weighted score of each segmental rhythm state in a set of segmental rhythm states of each of the plurality of analysis regions, wherein the weight of the segmental rhythm state of each of the analysis regions depends on the distance from the analysis region to the rhythm decision-making moment The time distance and/or the state corresponding to the analysis area. The method according to claim 9, characterized in that the short-term rhythm state characteristics further include: among the analysis areas whose state is the pause state of pressing or the state without filtering, the distance between the analysis area closest to the rhythm decision-making moment and the rhythm decision-making moment The temporal distance and the percentage of its segmental rhythmic state. The method according to claim 9, characterized in that, for an analysis region: The closer the analysis area is to the rhythm decision-making moment, the greater the weight assigned to the segmental rhythm state of the analysis area; and When the first aid treatment state of the analysis area is a compression pause state or the filtering state is a no-filtering state, assigning a first weight to the segmental rhythm state of the analysis area; When the first aid treatment state of the analysis area is a pressing state and the filtering state is a filtering state, assigning a second weight to the segmental rhythm state of the analysis area, wherein the second weight is smaller than the first weight; When the first aid treatment state of the analysis area is a pressing transition state and the filter state is a filtering state, a third weight is assigned to the segmental rhythm state of the analysis area, wherein the third weight is smaller than the second weight. The method according to claim 1, wherein the determining a rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics comprises: When the distribution of the segmental rhythm states represented by the long-term rhythm state features and the short-term rhythm state features are consistent, according to the Distribution determines rhythm decisions; When the distribution of the segmental rhythm state represented by the long-term rhythm state feature and the short-term rhythm state feature is inconsistent, the rhythm decision is determined according to the more reliable one of the two distributions, or, according to the The distribution of the segmental rhythm state embodied by the short-term rhythm state feature determines the rhythm decision, or outputs an uncertain rhythm decision. The method according to claim 12, characterized in that the reliability of the distribution of segmental rhythm states embodied in the long-term rhythm state characteristics and the short-term rhythm state characteristics respectively is related to the long-term rhythm analysis and short-term rhythm analysis The status of the analysis area used in each of them is related. The method according to claim 1, wherein the determining a rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics comprises: For each segmental rhythm state, according to the features corresponding to the long-term rhythm state and the segmental rhythm state, and the weighted combination of the short-term rhythm state features and the features corresponding to the segmental rhythm state, it is obtained a weighted score for the rhythm state of the segment; Rhythm decisions are determined based on weighted scores for each segment rhythm state. The method according to claim 1, characterized in that the method can be applied to a fixed cardiopulmonary resuscitation operation mode and a continuous cardiopulmonary resuscitation operation mode, and when applied to a continuous cardiopulmonary resuscitation operation mode, if the analysis area before the rhythm decision moment is not enough When used for long-term rhythm analysis, long-term rhythm analysis is not performed, and the rhythm decision is determined according to the preset long-term default initial rhythm state and the characteristics of the short-term rhythm state. The method of claim 1, wherein the analysis region has at least one of the following properties: Each of the analysis regions has the same time length or can change the time length as required; Any two of said analysis regions are continuous or discontinuous in time; Any two of the analysis regions partially overlap or do not overlap in time. A rhythm analysis and decision-making method for a defibrillator, characterized in that the method comprises: Acquiring the reference signal during the cardiopulmonary resuscitation of the target object and the original ECG signal of the target object; performing press detection on the reference signal to obtain a time-domain press event marker of the reference signal; Determining an instantaneous compression interval based on the time-domain compression event marker, and filtering the original ECG signal based on the instantaneous compression interval to obtain a filtered ECG signal; Divide the ECG signal of the target object into multiple analysis areas in time series, and perform rhythm analysis on each analysis area to obtain the segmental rhythm state of each analysis area, wherein the ECG signal of the target object The signal only includes the filtered ECG signal, or, the ECG signal of the target object includes the original ECG signal and the filtered ECG signal; Performing long-term rhythm analysis based on segmental rhythm states of a plurality of analysis regions within a first preset time period from the rhythm decision-making moment to obtain long-term rhythm state characteristics; Short-term rhythm analysis is performed based on the segmental rhythm state of at least one of the analysis regions within a second preset time period from the rhythm decision moment to obtain short-term rhythm state characteristics, wherein the first preset time period is longer than the first preset time period 2. Preset time period; A rhythm decision is determined based on the long-term rhythm state feature and the short-term rhythm state feature, and the rhythm decision is output. The method according to claim 17, wherein the pressing detection is performed on the reference signal to obtain the time-domain pressing event marker of the reference signal, comprising: performing time-domain analysis and/or frequency-domain analysis on the reference signal to obtain time-domain compression features and/or frequency-domain compression features; Performing press detection on the reference signal based on the time-domain press feature and/or the frequency-domain press feature, so as to obtain a time-domain press event marker of the reference signal. The method according to claim 17, wherein the long-term rhythm status characteristics and the short-term rhythm status characteristics include at least one of the following: The proportion of each segment rhythm state in the set of segment rhythm states of each of the plurality of analysis regions; When the segmental rhythm states of multiple analysis regions are sorted according to time, the proportion of the same segmental rhythm states for multiple consecutive times; In the corresponding state sets of each of the plurality of analysis regions, the time ratio of different states and/or the proportion of each segment rhythm state in different states; A weighted score of each segmental rhythm state in a set of segmental rhythm states of each of the plurality of analysis regions, wherein the weight of the segmental rhythm state of each of the analysis regions depends on the distance from the analysis region to the rhythm decision-making moment The time distance and/or the state corresponding to the analysis area. The method according to claim 17, wherein the determining a rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics comprises: When the distribution of the segmental rhythm states represented by the long-term rhythm state features and the short-term rhythm state features are consistent, according to the Distribution determines rhythm decisions; When the distribution of the segmental rhythm state represented by the long-term rhythm state feature and the short-term rhythm state feature is inconsistent, the rhythm decision is determined according to the more reliable one of the two distributions, or, according to the The distribution of the segmental rhythm state embodied by the short-term rhythm state feature determines the rhythm decision, or outputs an uncertain rhythm decision. The method according to claim 17, wherein the determining a rhythm decision based on the long-term rhythm state characteristics and the short-term rhythm state characteristics comprises: For each segmental rhythm state, according to the features corresponding to the long-term rhythm state and the segmental rhythm state, and the weighted combination of the short-term rhythm state features and the features corresponding to the segmental rhythm state, it is obtained a weighted score for the rhythm state of the segment; Rhythm decisions are determined based on weighted scores for each segment rhythm state. The method according to claim 17, characterized in that the method can be applied to a fixed cardiopulmonary resuscitation operating mode and a continuous cardiopulmonary resuscitation operating mode, and when applied to a continuous cardiopulmonary resuscitation operating mode, if the analysis area before the rhythm decision moment is not enough When used for long-term rhythm analysis, long-term rhythm analysis is not performed, and the rhythm decision is determined according to the preset long-term default initial rhythm state and the characteristics of the short-term rhythm state. A rhythm analysis and decision-making device, characterized in that the device includes a memory and a processor, the memory stores a computer program run by the processor, and the computer program is executed when the processor runs The rhythm analysis and decision-making method for a defibrillator according to any one of claims 1-22. The device according to claim 23, characterized in that the device further comprises a signal acquisition part, and the signal acquisition part is used to acquire reference signals related to chest compressions during cardiopulmonary resuscitation of the target object. 24. The device of claim 24, wherein said device is a defibrillator. A kind of storage medium, it is characterized in that, computer program is stored on the described storage medium, and described computer program is carried out as any one of claim 1-22 is used for the rhythm analysis of defibrillator and decision making method.

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