CN109620248B - Epileptic seizure monitoring system - Google Patents
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Abstract
The present application discloses seizure monitoring systems. The system comprises: a signal collector (101) for collecting a data signal on a target site of an epileptic patient, wherein the data signal at least comprises: acceleration signals, electrocardio signals and muscle electric signals; the signal processor (102) is in communication connection with the signal collector (101) and is used for processing the data signals; and the data analyzer (103) is in communication connection with the signal processor (102) and is used for judging whether the epileptic seizure occurs or not according to the processed data signal. By the method and the device, the problem that the epileptic seizure monitoring system in the related technology is low in accuracy rate of epileptic seizure monitoring is solved.
Description
Technical Field
The application relates to the technical field of information processing, in particular to an epileptic seizure monitoring system.
Background
Epilepsy is a chronic nervous system disease caused by sudden abnormal discharge of cerebral neurons, which can cause transient cerebral dysfunction and produce symptoms such as limb stiffness, abnormal tetany of limbs, absence and the like. The tonic-clonic epilepsy, commonly known as "grand mal", is an important research object for monitoring epileptic seizures, and is mainly characterized in that muscles continuously contract, twitch symmetrically or asymmetrically, and each time involve the same muscle group, accompanied by the actions of making a fist, bending the wrist and the like. The patients are often accidentally injured due to loss of mind, body control and respiratory arrest during epileptic seizures, and if the patients are not treated in time during the epileptic seizures, the nervous system injury can be aggravated due to brain inflammatory reaction, and more serious consequences can be caused. The epileptic seizure is paroxysmal, influences the normal work and life of the patient and causes the patient to generate anxiety. The epileptic seizure is accompanied by transient absence which can hardly be detected or severe clonus for a long time, and the conditions are complex and various and have no obvious rule. If the patient is not in public places or cared by no person during the attack, the patient is difficult to be found, and the attack history of the patient is difficult to be recalled afterwards.
The existing portable wearable epilepsy detection system adopting a single input signal has various types and different expression forms of the epilepsy, and the type of the epilepsy of a user may change along with time, treatment measures and the like, so that the epilepsy is difficult to accurately judge by only depending on the single signal such as an acceleration signal, an electrocardio signal and the like.
Aiming at the problem that the epileptic seizure monitoring system in the related art has low accuracy rate of epileptic seizure monitoring, no effective solution is provided at present.
Disclosure of Invention
The main objective of the present application is to provide a seizure monitoring system, so as to solve the problem in the related art that the seizure monitoring system has a low accuracy rate in monitoring seizures.
To achieve the above object, according to one aspect of the present application, there is provided a seizure monitoring system. The system comprises: the signal collector is used for collecting data signals on target parts of epileptics, wherein the data signals at least comprise: acceleration signals, electrocardio signals and muscle electric signals; the signal processor is in communication connection with the signal collector and is used for processing the data signals; and the data analyzer is in communication connection with the signal processor and is used for judging whether the epileptic seizure occurs in the epileptic patient according to the processed data signal.
Further, the signal collector includes: the first sensor is used for acquiring an acceleration signal on a target part of the epileptic; the second sensor is used for acquiring electrocardiosignals of the epileptic; and the third sensor is used for acquiring the muscle electric signal on the target part of the epileptic patient.
Further, the seizure monitoring system includes: the early warning device is in communication connection with the data analyzer and is used for triggering reminding information to remind a target object of the epileptic seizure under the condition that the data analyzer judges the epileptic seizure of the epileptic seizure, wherein the reminding information is in a mode of at least one of the following modes: the method comprises the following steps of information reminding, voice reminding, photoelectric reminding or sending a voice control instruction to a communication tool under the control of the early warning device under the condition that the epileptic carries the communication tool; through communication tool voice broadcast target information to remind epileptic patient's personnel next to epileptic patient's epileptic seizure.
Further, the data analyzer is further configured to compare the processed data signal with a pre-stored data signal at the time of the epileptic seizure to identify the type of the epileptic seizure, if the epileptic seizure of the epileptic seizure is determined; the early warning device is further used for selecting a reminding mode according to the type of the epileptic seizure and triggering reminding information according to the selected reminding mode, wherein different types of the epileptic seizure correspond to different reminding modes.
Further, the seizure monitoring system further includes: the weight adjusting module is used for setting the weight of signals in the data signals, wherein the weight of the acceleration signals is greater than that of the muscle electric signals, and the weight of the myoelectric signals is greater than that of the electrocardio signals.
Further, the weight adjusting module is further configured to, when the number of epileptic seizures of the epileptic patient exceeds a preset number, adjust the weight of the signal in the data signal according to the type of epileptic seizures of the epileptic patient, increase the sampling rate of the signal with the highest weight, and decrease the sampling rate of the signal with the lowest weight.
Further, the target site includes at least one of: an arm or a wrist.
Further, the signal processor is further configured to invoke different algorithms to analyze the data signal according to the difference of the target portion.
Further, the seizure monitoring system further includes: and the storage module is used for storing the acquired data signals on the target part of the epileptic patient and storing the seizure type of the epileptic patient under the condition that the epileptic seizure of the epileptic patient is judged.
Further, the data analyzer includes: the first processing module is used for performing windowing processing on the data signals according to a preset length to obtain a plurality of sub-signals of a target axis; the second processing module is used for processing the plurality of sub-signals of the target axis to obtain two characteristic values; and the judging module is used for judging whether the epileptic patient has the epileptic seizure or not based on the two characteristic values.
By this application, the following devices are employed: the signal collector gathers the data signal on epileptic target site, and wherein, the data signal includes at least: acceleration signals, electrocardio signals and muscle electric signals; the signal processor (102) is in communication connection with the signal collector and is used for processing the data signals; the data analyzer is in communication connection with the signal processor and is used for judging whether the epileptic seizure occurs in the epileptic patient according to the processed data signal. By the method and the device, the problem that the epileptic seizure monitoring system in the related technology is low in accuracy rate of monitoring epileptic seizures is solved, and the problem that the epileptic seizure monitoring system in the related technology is low in accuracy rate of monitoring epileptic seizures is solved. By acquiring the acceleration signal, the electrocardiosignal and the muscle electric signal and processing the acceleration signal, the electrocardiosignal and the muscle electric signal, whether the epileptic seizure occurs or not is monitored, and the effect of improving the monitoring accuracy of the epileptic seizure is further achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a schematic diagram of a seizure monitoring system provided in accordance with an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present application, a seizure monitoring system is provided.
Fig. 1 is a schematic diagram of a seizure monitoring system according to an embodiment of the present application. As shown in fig. 1, the system includes the following devices:
a signal collector 101, configured to collect a data signal on a target portion of an epileptic patient, where the data signal at least includes: acceleration signals, electrocardio signals and muscle electric signals;
the target site includes at least one of: an arm or a wrist.
Optionally, in order to acquire the accuracy of the signal, in the seizure monitoring system provided in the embodiment of the present application, the signal acquirer 101 includes: the first sensor is used for acquiring an acceleration signal on a target part of the epileptic; the second sensor is used for acquiring electrocardiosignals of the epileptic; and the third sensor is used for acquiring the muscle electric signal on the target part of the epileptic.
For example, the first sensor is a three-axis sensor, and the three-axis sensor is used for continuously acquiring the acceleration signal at the wrist of the epileptic within 1 second.
The signal processor 102 is in communication connection with the signal collector 101 and is used for processing the data signals;
for example, the data signal is an acceleration signal, and the signal processor determines a signal of a target axis from the acceleration signals of three axes, wherein the amplitude of the signal of the target axis is greater than the amplitudes of the signals of the remaining two axes.
In order to ensure the accuracy of subsequent judgment on whether the epileptic seizure occurs, in the embodiment of the application, one axis with the most obvious fluctuation degree is selected from three-axis signals of the three-axis sensor to serve as a main energy axis, namely a target axis, and signals collected on the target axis are used as signals of the target axis. The signal of the axis with the most obvious fluctuation degree is used as the signal of subsequent processing, so that the accuracy of monitoring the epileptic seizure depending on data is ensured, and the accuracy of subsequent judgment of the epileptic seizure is ensured. And then carrying out windowing processing on the signal of the target axis according to a preset length to obtain a plurality of sub-signals of the target axis. For example, if the length of the acquired target axis signal is 150 frames and the preset length is 50, performing windowing on the acquired target axis signal to obtain 3 sub-signals of the target axis, where each sub-signal includes 50 data.
Optionally, in order to ensure accuracy of obtaining the feature value, in this embodiment of the present application, processing the multiple sub-signals of the target axis to obtain two feature values includes: performing autocorrelation processing on each sub-signal of the target axis; constructing a Topritz matrix based on the processed sub-signals, and determining an inverse matrix of the constructed Topritz matrix; and calculating the spatial decorrelation parameters based on the inverse matrix to obtain two characteristic values.
Wherein the performing of the autocorrelation process on each sub-signal of the target axis includes: performing autocorrelation processing on each sub-signal of the target axis by adopting an algorithm I, wherein the algorithm I is as follows:
and acc (i) is ith data of the sub-signals, m is the number of data included in each sub-signal, n is an integer, and a (n) is nth processed data in the sub-signals.
Wherein constructing a Topritz matrix based on the processed sub-signals and determining an inverse matrix of the constructed Topritz matrix comprises: the row vector formed by the processed sub-signals is [ a ]1,a2···an]And taking the first k values to construct a symmetrical Toeplitz matrix as follows:
Then, the multiple sub-signals of the target axis are processed to obtain two characteristic values.
In the embodiment of the present application, calculating a spatial decorrelation parameter based on an inverse matrix, and obtaining two eigenvalues includes: taking the column vector L of the inverse matrix as [ a ]j aj+1…aj+k-1]TWherein, in the step (A),
calculating X1＝T-1×L，X2＝-X1(ii) a Obtaining a first characteristic value c1＝X2(1) And a second characteristic value c2＝X2(j)。
And the data analyzer 103 is in communication connection with the signal processor 102 and is used for judging whether the epileptic patient has epileptic seizure according to the processed data signal.
And determining whether the epileptic patient has the epileptic seizure based on the two characteristic values.
Optionally, in an embodiment of the present application, the two feature values are a first feature value and a second feature value, the preset threshold includes a first threshold, a second threshold and a third threshold, where the first threshold is smaller than the second threshold, and the second threshold is smaller than the third threshold, and determining whether the epileptic patient has the epileptic seizure based on the two feature values includes: and determining the epileptic seizure of the epileptic patient in the event that the first characteristic value is greater than the first threshold value and the second characteristic value is greater than the second threshold value, or in the event that the first characteristic value is greater than the first threshold value and the second characteristic value is greater than the first threshold value and less than the second threshold value.
Optionally, in an embodiment of the present application, the method further includes: and determining that the epileptic patient has not suffered an epileptic seizure when the absolute value of the first characteristic value is less than a first threshold value and the absolute value of the second characteristic value is less than a second threshold value, or when the first characteristic value is less than a third threshold value and the second characteristic value is greater than the first threshold value and less than the second threshold value, or when the first characteristic value is less than the third threshold value and the absolute value of the second characteristic value is greater than the second threshold value.
Optionally, in an embodiment of the present application, the method further includes: when the absolute value of the first characteristic value is smaller than a first threshold value and the absolute value of the second characteristic value is smaller than a second threshold value, determining that the limb of the epileptic patient moves randomly; determining that the limb of the epileptic patient is in stable motion when the first characteristic value is less than a third threshold value, and the second characteristic value is greater than the first threshold value and less than a second threshold value; and when the first characteristic value is smaller than a third threshold value and the absolute value of the second characteristic value is larger than a second threshold value, determining that the limb of the epileptic patient is in variable-frequency motion.
For example, the first characteristic value is C1, the second characteristic value is C2, and C1 and C2 are compared with preset thresholds th1, th2 and th 3.
When | a1|＜th1,|a2|＜th2Judging the limb to move randomly;
when a is1＞th1,a2＞th2Judging the frequency conversion convulsion of the limb;
when a is1＞th1,th1＜a2＜th2Judging the stable convulsion of the limbs;
when a is1＜th3，|a2|＞th2Judging the limb movement to be variable frequency movement;
when a is1＜th3,th1＜a2＜th2Judging the limb to move stably;
the random, variable frequency and steady movements were judged as normal, and variable frequency and steady tics were judged as seizures. Among them, the thresholds th1, th2 and th3 are derived from a large amount of data by statistical analysis. For example, th1 is 0.3, th1 is 1.2, and th1 is 1.5.
The seizure monitoring system provided by the embodiment of the application can be applied to a low-operation method for detecting whether the seizure of the tonic-clonus type occurs in real time according to different characteristics of triaxial acceleration of abnormal tetany of limbs and other normal limb activities during the seizure of the tonic-clonus type. The method does not relate to the relevant content of machine learning, adopts a spatial decorrelation process to extract the features, and compares the extracted features with a threshold value obtained by statistical analysis to realize the distinction between normal physiological activities and epileptic seizures. According to the method, on the premise of ensuring the detection accuracy, the calculated amount is relatively small, the requirements on energy consumption, operation speed and the like of hardware equipment are low, namely, the epileptic seizure monitoring method provided by the embodiment of the application can detect whether the epileptic seizure exists in the epileptic patient, the complexity of epileptic seizure detection is simplified, and the accuracy of epileptic seizure detection is improved.
After determining the epileptic seizure of the epileptic patient, in order to ensure the safety of the epileptic patient, in an embodiment of the present application, after determining the epileptic seizure of the epileptic patient, the method further includes: triggering reminding information to a target object so as to remind the target object of epileptic seizure, wherein the reminding information is at least one of the following modes: information reminding, voice reminding and calling reminding; or, under the condition that the epileptic carries the communication tool, controlling to send a voice control instruction to the communication tool; target information is broadcasted through a communication tool in a voice mode so as to remind people around the epileptic patient of the epileptic seizure.
That is, in the seizure monitoring system provided in the embodiments of the present application, the seizure monitoring system includes: the early warning device is in communication connection with the data analyzer 103, and is configured to trigger the reminding information to remind the target object of the epileptic seizure when the data analyzer 103 determines that the epileptic seizure of the epileptic seizure is occurring, where the reminding information is in a form of at least one of: the system comprises an information reminding device, a voice reminding device, a photoelectric reminding device or a warning device, wherein the warning device controls to send a voice control instruction to a communication tool under the condition that an epileptic carries the communication tool; target information is broadcasted through a communication tool in a voice mode so as to remind people around the epileptic patient of the epileptic seizure.
Through the scheme, after the epileptic seizure of the epileptic is determined, the reminding information is triggered to remind a target object, such as a parent of the epileptic, or the epileptic is controlled to carry a communication tool to report the target information so as to remind a current person beside the epileptic of the epileptic to the epileptic seizure, so that the safety of the epileptic is timely protected by the parent or the current person beside the epileptic, and the epileptic is prevented from being secondarily injured.
The seizure monitoring system provided by the embodiment of the application adopts the following devices: a signal collector 101, configured to collect a data signal on a target portion of an epileptic patient, where the data signal at least includes: acceleration signals, electrocardio signals and muscle electric signals; the signal processor 102 is in communication connection with the signal collector 101 and is used for processing the data signals; and the data analyzer 103 is in communication connection with the signal processor 102 and is used for judging whether the epileptic patient has epileptic seizure according to the processed data signal. By the method and the device, the problem that the epileptic seizure monitoring system in the related technology is low in accuracy rate of monitoring epileptic seizures is solved, and the problem that the epileptic seizure monitoring system in the related technology is low in accuracy rate of monitoring epileptic seizures is solved. By acquiring the acceleration signal, the electrocardiosignal and the muscle electric signal and processing the acceleration signal, the electrocardiosignal and the muscle electric signal, whether the epileptic seizure occurs or not is monitored, and the effect of improving the monitoring accuracy of the epileptic seizure is further achieved.
Optionally, in order to protect the safety of the epileptic patient and avoid the epileptic patient from secondary injury, in the epileptic seizure monitoring system provided in the embodiment of the present application, the data analyzer 103 is further configured to compare the processed data signal with a pre-stored data signal at the time of epileptic seizure to identify the type of epileptic seizure of the epileptic patient, when the epileptic seizure of the epileptic patient is determined; the early warning device is also used for selecting a reminding mode according to the type of the epileptic seizure and triggering reminding information according to the selected reminding mode, wherein different types during the epileptic seizure correspond to different reminding modes.
By the scheme, different seizure types correspond to different reminding modes during epileptic seizure, so that more targeted reminding is realized.
Optionally, in the seizure monitoring system provided in the embodiment of the present application, the seizure monitoring system further includes: the weight adjusting module is used for setting the weight of signals in the data signals, wherein the weight of the acceleration signals is greater than that of the muscle electric signals, and the weight of the myoelectric signals is greater than that of the electrocardio signals.
Optionally, in the seizure monitoring system provided in the embodiment of the present application, the weight adjusting module is further configured to adjust the weight of the signal in the data signal according to the type of the seizure of the epileptic patient when the number of seizures of the epileptic patient exceeds a preset number, and increase the sampling rate of the signal with the highest weight and decrease the sampling rate of the signal with the lowest weight.
Optionally, in the seizure monitoring system provided in the embodiment of the present application, the signal processor 102 is further configured to invoke different algorithms to analyze the data signal according to different target portions.
In order to manage the data of the epileptic patient more comprehensively and analyze the seizure condition of the epileptic patient, the epileptic seizure monitoring system in the epileptic seizure monitoring system provided by the embodiment of the application further comprises: and the storage module is used for storing the acquired data signals on the target part of the epileptic patient and storing the seizure type of the epileptic patient under the condition that the epileptic seizure of the epileptic patient is judged.
Through the scheme, the data of the epileptic patient are managed more comprehensively, so that the epileptic patient can be analyzed for seizure conditions later, and a more effective treatment scheme for the epileptic patient can be provided.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to one or more, and the epilepsy is monitored by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a storage medium having a program stored thereon, which when executed by a processor implements the seizure monitoring system.
An embodiment of the invention provides a processor for executing a program, wherein the program executes the seizure monitoring system during execution.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therewith, including but not limited to disk storage, CD-ROM, optical storage, and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus systems, and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (9)
1. Seizure monitoring system, characterized in that it comprises:
a signal collector (101) for collecting a data signal on a target site of an epileptic patient, wherein the data signal comprises at least: acceleration signals, electrocardio signals and muscle electric signals;
the signal processor (102) is in communication connection with the signal collector (101) and is used for processing the data signal;
a data analyzer (103) communicatively connected to the signal processor (102) for determining whether the epileptic seizure is occurring based on the processed data signal;
wherein the data analyzer (103) comprises:
the first processing module is used for performing windowing processing on a signal of a target axis according to a preset length to obtain a plurality of sub-signals of the target axis, wherein the signal of the target axis is determined by an acceleration signal of three axes, and the amplitude of the signal of the target axis is greater than the amplitudes of the signals of the other two axes;
the second processing module is used for processing the plurality of sub-signals of the target axis to obtain two characteristic values;
a determination module, configured to determine whether the epileptic patient has an epileptic seizure based on the two feature values;
the second processing module is further used for performing autocorrelation processing on each sub-signal of the target axis; constructing a Topritz matrix based on the processed sub-signals, and determining an inverse matrix of the constructed Topritz matrix; calculating a spatial decorrelation parameter based on the inverse matrix to obtain two characteristic values;
wherein the second processing module is further configured to: performing an autocorrelation process on each sub-signal of the target axis using an algorithm, wherein,
the first algorithm is as follows:
acc (i) is the ith data of the sub-signal, m is the number of data included in each sub-signal, n is an integer, anThe processed data of the nth sub signal is obtained;
the second processing module is further configured to: the row vector formed by the processed sub-signals is [ a ]1,a2···an]And taking the first k values to construct a symmetrical Toeplitz matrix as follows:
determining the inverse matrix corresponding to the Topritz matrix as T-1Wherein k is determined according to the number of points of the conventional limb movement in the next period at the current sampling rate, and k < n;
the second processing module is further configured to take a column vector L ═ b of the inverse matrixj bj+1 … bj+k-1]TWherein, in the step (A),
calculating X1＝T-1×L，X2＝-X1Obtaining a first characteristic value c1＝X2(1) And a second characteristic value c2＝X2(j)。
2. The seizure monitoring system according to claim 1, wherein the signal collector (101) includes:
the first sensor is used for acquiring an acceleration signal on a target part of the epileptic;
the second sensor is used for acquiring electrocardiosignals of the epileptic;
and the third sensor is used for acquiring the muscle electric signal on the target part of the epileptic patient.
3. The seizure monitoring system of claim 1, comprising:
the early warning device is in communication connection with the data analyzer (103) and is used for triggering reminding information to remind a target object of the epileptic seizure when the data analyzer (103) judges the epileptic seizure of the epileptic seizure, wherein the reminding information is at least one of the following modes: the method comprises the following steps of information reminding, voice reminding, photoelectric reminding or sending a voice control instruction to a communication tool under the control of the early warning device under the condition that the epileptic carries the communication tool; through communication tool voice broadcast target information to remind epileptic patient's personnel next to epileptic patient's epileptic seizure.
4. The seizure monitoring system according to claim 3,
the data analyzer (103) is further used for comparing the processed data signal with a pre-stored data signal at the time of the epileptic seizure to identify the type of the epileptic seizure under the condition that the epileptic seizure of the epileptic seizure is judged;
the early warning device is further used for selecting a reminding mode according to the type of the epileptic seizure and triggering reminding information according to the selected reminding mode, wherein different types of the epileptic seizure correspond to different reminding modes.
5. The seizure monitoring system according to claim 4, further comprising:
the weight adjusting module is used for setting the weight of signals in the data signals, wherein the weight of the acceleration signals is greater than the weight of the muscle electric signals, and the weight of the muscle electric signals is greater than the weight of the electrocardio signals.
6. The system according to claim 5, wherein the weight adjusting module is further configured to adjust the weight of the signal in the data signal according to the type of the epileptic seizure of the epileptic patient when the number of epileptic seizures of the epileptic patient exceeds a preset number, and increase the sampling rate of the signal with the highest weight and decrease the sampling rate of the signal with the lowest weight.
7. The seizure monitoring system of claim 1, wherein the target site includes at least one of: an arm or a wrist.
8. The seizure monitoring system of claim 1 wherein the signal processor (102) is further configured to invoke different algorithms to analyze the data signal based on the target site.
9. The seizure monitoring system of claim 1, further comprising:
and the storage module is used for storing the acquired data signals on the target part of the epileptic patient and storing the seizure type of the epileptic patient under the condition that the epileptic seizure of the epileptic patient is judged.
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