CN120419978B - Emergency patient consciousness state monitoring system based on bioelectric signals - Google Patents
Emergency patient consciousness state monitoring system based on bioelectric signalsInfo
- Publication number
- CN120419978B CN120419978B CN202510933825.7A CN202510933825A CN120419978B CN 120419978 B CN120419978 B CN 120419978B CN 202510933825 A CN202510933825 A CN 202510933825A CN 120419978 B CN120419978 B CN 120419978B
- Authority
- CN
- China
- Prior art keywords
- signal
- coupling
- time
- state monitoring
- consciousness state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
本发明涉及生物意识状态监测技术领域，具体涉及基于生物电信号的急诊患者意识状态监测系统，本发明系统通过脑电信号采集模块及信号提取模块，实现θ节律信号、α波信号、γ波振荡信号的获取；通过相位振幅耦合事件时间位置定位模块，获取有效相位振幅耦合事件及其发生时间位置；基于患者意识状态监测模块，得到患者意识状态监测指标，并将患者意识状态监测指标与时序拟合，完成患者意识状态监测。通过本发明可以精准定位θ‑γ跨脑区相位振幅耦合（PAC）事件，以及突破传统技术对PAC事件时空定位模糊、动态追踪滞后的局限，实现急诊患者意识状态的毫秒级高分辨率监测，为意识障碍的实时干预提供可量化神经电生理依据。
The present invention relates to the technical field of biological consciousness state monitoring, and specifically to an emergency patient consciousness state monitoring system based on bioelectric signals. The system of the present invention uses an EEG signal acquisition module and a signal extraction module to acquire θ rhythm signals, α wave signals, and γ wave oscillation signals; uses a phase amplitude coupling event time position positioning module to acquire effective phase amplitude coupling events and their occurrence time positions; and uses a patient consciousness state monitoring module to obtain patient consciousness state monitoring indicators, and fits the patient consciousness state monitoring indicators with a time series to complete patient consciousness state monitoring. The present invention can accurately locate θ-γ cross-brain region phase amplitude coupling (PAC) events, and overcome the limitations of traditional technologies in fuzzy spatiotemporal positioning of PAC events and delayed dynamic tracking, thereby achieving millisecond-level high-resolution monitoring of emergency patient consciousness states and providing a quantifiable neuroelectrophysiological basis for real-time intervention in consciousness disorders.
Description
Technical Field
The invention relates to the technical field of biological consciousness state monitoring, in particular to an emergency patient consciousness state monitoring system based on bioelectricity signals.
Background
Neurophysiologic assessment of consciousness in emergency treatment field mainly depends on spectral quantification analysis of electroencephalogram (EEG), for example, judging the separation degree of the consciousness-sleep cycle through alpha wave and slow wave power ratio or brain electrical complexity index (such as approximate entropy and Lempel-Ziv complexity), and the accuracy of distinguishing unconsciousness from consciousness states can reach 82%, but the distinguishing specificity of micro consciousness states from plant states is insufficient.
The essence of this limitation is that existing methods fail to resolve the multiscale kinetic features of the critical neurological subsystems associated with consciousness, especially the dynamic functional link reorganization mechanisms of the cortical-thalamic-brainstem network. Most of the prior art is based on fourier transform or wavelet analysis, in order to maintain spectral resolution, its time localization accuracy cannot capture transient functional connection reconstruction events in the cortex-thalamus loop with duration less than 500ms, such as cross-band Phase Amplitude Coupling (PAC) between forehead Shebo-segment oscillations and thalamus plate kernel group θ rhythms for 300-400ms, which has proven to be a hallmark feature of residual cognitive function in the micro-conscious state, but PAC strength is greatly underestimated due to time localization accuracy problems.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an emergency patient consciousness state monitoring system based on bioelectric signals.
According to the bioelectric signal-based emergency patient consciousness state monitoring system provided by the embodiment of the application, the adopted technical scheme specifically comprises the following steps:
the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals of a thalamus region, a parietal lobe region and a forehead lobe region;
The signal extraction module is used for extracting theta rhythm signals from the brain electrical signals of the middle thalamus region, extracting alpha wave signals from the brain electrical signals of the top lobe region and separating gamma wave oscillation signals from the brain electrical signals of the forehead lobe region;
The phase amplitude coupling event time position positioning module is used for analyzing the coupling probability and the coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window and acquiring an effective phase amplitude coupling event and the occurrence time position thereof;
And the patient consciousness state monitoring module is used for analyzing the occurrence density and the duration of the effective phase amplitude coupling event, analyzing the correlation between the alpha wave signal and the gamma wave oscillation signal, obtaining a patient consciousness state monitoring index, fitting the patient consciousness state monitoring index with a time sequence, and completing patient consciousness state monitoring.
In some embodiments of the invention, the signal extraction module comprises:
The theta rhythm signal extraction unit is used for enhancing the theta rhythm contribution of the middle thalamus region according to the real-time power of the theta frequency band of the brain electrical signal of the middle thalamus region, and then extracting a low-frequency oscillation component through a 4-7Hz band-pass filter to obtain a theta rhythm signal;
The alpha wave signal extraction unit is used for extracting an alpha wave signal from the brain electrical signal of the top leaf area through a band-pass filter of 8-13Hz and enhancing the alpha wave signal according to the alpha frequency band real-time power;
And the gamma wave oscillation signal separation unit is used for separating the gamma wave oscillation signal from the electroencephalogram signal in the frontal lobe area by combining a rapid independent component analysis algorithm and short-time Fourier transformation.
In some embodiments of the present invention, the gamma wave oscillation signal separation unit is configured to:
Carrying out component separation on the electroencephalogram signals of the forehead lobe area for a fixed number of times by utilizing a rapid independent component analysis algorithm, and carrying out frequency domain conversion on each layer of component signals in the electroencephalogram signals of the forehead lobe area to time domain signals by utilizing short-time Fourier transformation to obtain independent signal components of signal segments in each short time window;
And analyzing the pearson correlation coefficient of the energy ratio of the alpha wave signal and the energy ratio of the gamma wave oscillation signal of each independent component signal in all short time windows, and extracting the frequency gradient of each independent component signal in all short time windows to obtain the gamma wave oscillation signal.
In some embodiments of the invention, the phase-amplitude coupled event time position location module comprises:
The signal preprocessing unit is used for respectively carrying out short-time Fourier transform on the theta rhythm signal and the gamma wave oscillation signal;
the coupling probability analysis unit is used for analyzing the dispersion of the gamma wave oscillation signal distribution in the phase interval divided according to the theta rhythm signal in each time window to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window;
The coupling direction analysis unit is used for analyzing the change sequence of the change of the theta rhythm signal and the change of the gamma wave oscillation signal in each time window to obtain the coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window;
And the effective phase-amplitude coupling event positioning unit is used for acquiring an effective phase-amplitude coupling event and the occurrence time position thereof according to the coupling probability and the coupling direction.
In some embodiments of the invention, the coupling probability analysis unit is configured to:
extracting all instantaneous phases of the theta rhythm signal in each time window;
calculating the instantaneous energy value of the theta rhythm signal to obtain the weight of each instantaneous phase;
dividing all phases of the theta rhythm signal into a plurality of phase intervals;
analyzing the dispersion of the gamma wave oscillation signal distribution in the phase interval where each instantaneous phase is located;
And traversing all instantaneous phases in each time window according to the weight and the divergence to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window.
In some embodiments of the invention, the coupling probability analysis unit is further configured to extract all instantaneous phases of the θ rhythm signal within each time window, further comprising:
performing Hilbert transformation on the theta rhythm signal in each time window, and extracting all instantaneous phases of the theta rhythm signal in each time window, wherein the method further comprises the following steps:
And eliminating the trip point by using a phase unwrapping technology to obtain all corrected instantaneous phases of the theta rhythm signal in each time window.
In some embodiments of the invention, the effective phase-amplitude coupled event localization unit is configured to:
presetting a coupling probability threshold;
Judging whether the coupling probability corresponding to each time window is larger than the coupling probability threshold value or not, and judging whether the coupling direction corresponding to each time window is that the theta-rhythm signal changes before the gamma-wave oscillation signal changes or not;
if the phase and amplitude coupling events are satisfied at the same time, the time window is a time window with the phase and amplitude coupling events, and the time window is recorded as a coupling time window;
Calculating the standard deviation of the amplitude of the gamma wave oscillation signal in the coupling time window, and marking the standard deviation as the coupling strength;
Presetting a coupling strength base line;
If the coupling strengths corresponding to the continuous 3 coupling event windows exceed the coupling strength baseline by 2 times, the time periods corresponding to the continuous coupling event windows are marked as the time periods with effective phase amplitude coupling events.
In some embodiments of the invention, the system further comprises:
And the super sampling module is used for starting a super sampling mode for a time period with an effective phase and amplitude coupling event and capturing full-band nerve oscillation details.
In some embodiments of the invention, the patient awareness status monitoring module is configured to:
Counting the occurrence times of the effective phase-amplitude coupling event in unit time, and recording the occurrence times as the occurrence density of the effective phase-amplitude coupling event;
calculating the average duration of all the effective phase-amplitude coupling events in unit time, and recording the average duration as the duration of the effective phase-amplitude coupling events;
analyzing the correlation of the alpha wave signal and the gamma wave oscillation signal to obtain the change trend of the consciousness state of the patient;
obtaining a time attenuation factor according to the number of all unit time from the monitoring start to the current unit time;
Combining the occurrence density, the duration time, the patient consciousness state change trend and the time attenuation factor to obtain a patient consciousness state monitoring index;
fitting the patient consciousness state monitoring index with time sequence to complete patient consciousness state monitoring.
In some embodiments of the present invention, the electroencephalogram signal acquisition module is further configured to acquire a triaxial accelerometer signal, and construct a respiratory rhythm harmonic model through the triaxial accelerometer signal, so as to eliminate 200-800 ms-level micro-motion artifacts of the electroencephalogram signal.
Compared with the prior art, the emergency patient consciousness state monitoring system based on the bioelectric signals has the following beneficial effects:
The invention acquires, extracts and directionally enhances the theta rhythm signal of the middle thalamus region, the alpha wave signal of the top lobe region and the gamma wave oscillation signal of the brain signal of the forehead lobe region by arranging an electroencephalogram signal acquisition module and a signal extraction module, analyzes the coupling probability and the coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window by a phase amplitude coupling event time position positioning module to acquire an effective phase amplitude coupling event and the occurrence time position thereof, and analyzes the occurrence density and the duration of the effective phase amplitude coupling event and the correlation of the alpha wave signal and the gamma wave oscillation signal based on a patient consciousness state monitoring module to acquire a patient consciousness state monitoring index, and completes the patient consciousness state monitoring by fitting the patient consciousness state monitoring index with time sequence. According to the invention, dynamic time-frequency coupling analysis is introduced, which comprises long and short time window coordination, divergence evaluation of gamma waves in a specific phase of theta waves and phase lead detection, so that theta-gamma cross-brain-region Phase Amplitude Coupling (PAC) events are precisely positioned, and the limitation of time-space positioning ambiguity and dynamic tracking hysteresis of PAC events in the traditional technology is broken through by constructing time sequence attenuation weighting indexes fusing PAC event density, duration and alpha-gamma correlation, thereby realizing millisecond-level high-resolution monitoring of the consciousness state of emergency patients and providing quantifiable nerve electrophysiological basis for real-time intervention of consciousness disturbance.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the basic components of a bioelectric signal-based monitoring system for the consciousness state of an emergency patient according to an embodiment of the present invention;
Fig. 2 is a schematic diagram of an electroencephalogram according to an embodiment of the present invention;
fig. 3 is a schematic diagram of EEG electrode distribution according to an embodiment of the invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of the bioelectrical signal-based emergency patient consciousness state monitoring system according to the present invention, which is provided by the present invention, with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Such as the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, 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 article or apparatus. Without further limitation, the statement "comprises an" or "comprising" does not exclude that an additional identical element is present in an article or device comprising the element.
The following specifically describes a specific scheme of the emergency patient consciousness state monitoring system based on bioelectric signals provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a basic component of a bioelectric signal-based monitoring system for the consciousness state of an emergency patient according to an embodiment of the present invention is shown.
As shown in fig. 1, the system for monitoring the consciousness state of an emergency patient based on bioelectric signals according to an embodiment of the present invention mainly includes an electroencephalogram signal acquisition module 10, a signal extraction module 20, a phase-amplitude coupled event time location module 30, a supersampling module 40 and a patient consciousness state monitoring module 50. Wherein:
the electroencephalogram signal acquisition module 10 is used for acquiring electroencephalogram signals of a middle thalamus region, a top lobe region and a forehead lobe region. Further, the electroencephalogram signal acquisition module 10 includes an EEG electrode cap 11 and a data storage unit 12. Specifically, the electroencephalogram signal acquisition operation method comprises the steps of adjusting an emergency patient to a semi-lying position (the backrest is inclined by 45 degrees), customizing a groove by a headrest to reduce neck micro-motion, wearing an EEG electrode cap 11 for the patient to ensure the coverage density of the EEG electrodes in the forehead area and the central top area, reducing impedance by using conductive paste, and adopting a 256-channel high-density EEG electrode cap 11 (the sampling rate is more than or equal to 5000 Hz), wherein the coverage density of the EEG electrodes in the forehead area and the central top area is mainly enhanced.
256-Channel high-density EEG refers to a system for simultaneously recording brain electrical activity by using 256 electrodes, wherein each electrode can independently acquire brain electrical signals at corresponding positions, and FIG. 2 is a schematic diagram of brain electrical signals, and the brain electrical signals acquired by the electrodes positioned in the middle thalamus area, the top lobe area and the forehead lobe area are respectively screened out from the 256 channels. FIG. 3 is a schematic illustration of electrode distribution in which the mesothalamus region may contain C3, cz, C4, O1, O2, the parietal region may contain T3, T4, T5, T6, P3, P4, pz, CP1, CP2, and the prefrontal region may contain F7, F3, fz, F4, F8, FC1, FC2, fp1, fp2. The electroencephalogram signals of 256 electrode positions are acquired in real time, and the acquired electroencephalogram signals are stored in the data storage unit 12 according to the time sequence corresponding to the signal acquisition positions.
In some embodiments of the present invention, the electroencephalogram signal acquisition module 10 further includes a triaxial accelerometer 13, the triaxial accelerometer 13 is fixed on the chest, the baseline motion signal is calibrated, the triaxial accelerometer 13 signal is acquired, and the electroencephalogram signal acquisition module 10 is configured to construct a respiratory rhythm harmonic model through the triaxial accelerometer 13 signal, and remove 200-800 ms-level micro-motion artifacts of all acquired electroencephalogram signals to obtain denoised electroencephalogram signals, and the known techniques are not repeated. And stores the acquired denoised electroencephalogram signals corresponding to the signal acquisition positions in the data storage unit 12 according to the time sequence order. If no special description exists, the electroencephalogram signals used later are all denoised electroencephalogram signals.
The signal extraction module 20 is used for extracting a theta rhythm signal from an electroencephalogram signal of a middle thalamus region, extracting an alpha wave signal from an electroencephalogram signal of a top lobe region, and separating a gamma wave oscillation signal from an electroencephalogram signal of a forehead lobe region.
When consciousness occurs, the theta wave phases of the plate kernel group in the middle thalamus region are synchronized first, and gamma wave oscillation signals (60-150 Hz) in the forehead She Waice region are driven to be active through cross-frequency coupling (locking of the theta wave phases and the gamma wave amplitudes in the forehead lobe region), and the coupling has definite directivity (middle thalamus region- & gt forehead lobe region). The theta rhythm signal of the middle thalamus area plate kernel group refers to the neural oscillation activity of the theta wave frequency band (4-7 Hz) participated by the middle thalamus area plate kernel and the adjacent inner side kernel group, and the core function is to integrate the cortical activity through a phase synchronization mechanism of the cross brain area so as to form coherent conscious experience and memory processing.
In a relaxed state of normal adults, particularly in the absence of psychological activities, alpha waves (8-13 Hz) can be spontaneously observed. Particularly in the closed eye state, the alpha wave is apparent at the top lobe region position.
Based on the above analysis, in the embodiment of the present invention, the signal extraction module 20 is provided to extract θ rhythm signals from the brain electrical signals of the middle thalamus region, extract α wave signals from the brain electrical signals of the top lobe region, and separate γ wave oscillation signals from the brain electrical signals of the forehead lobe region, as basic data for analyzing the consciousness state of the emergency patient. Further, the signal extraction module 20 includes a θ rhythm signal extraction unit 21, an α wave signal extraction unit 22, and a γ wave oscillation signal separation unit 23. Wherein:
And a theta rhythm signal extraction unit 21 for enhancing the theta rhythm contribution of the middle thalamus region according to the real-time power of the theta frequency band of the brain electrical signal of the middle thalamus region, and then extracting the low frequency oscillation component through a 4-7Hz band-pass filter to obtain the theta rhythm signal. The method is specifically configured to perform mean value filtering on all the brain electrical signals of a middle thalamus region, normalize the real-time power values of theta frequency bands (4-8 Hz) of the brain electrical signals acquired by each electrode of the middle thalamus region according to the standard brain atlas, and then use the normalized values as weights of the brain electrical signals corresponding to the positions of each electrode, weight and sum the brain electrical signals filtered by each channel by the weights, so that the contribution of theta rhythm signals of the middle thalamus region can be directly enhanced, then use a 4-7Hz band-pass filter to extract low-frequency oscillation components, and inhibit other frequency interference, thereby obtaining time domain signals containing the theta rhythm signals.
And an alpha wave signal extraction unit 22 for extracting an alpha wave signal from the brain electrical signal of the top lobe region through a band-pass filter of 8-13Hz, and enhancing the alpha wave signal according to the alpha frequency band real-time power. The method is specifically configured to directly extract alpha wave signals from all the electroencephalogram signals at the top lobe region by using a band-pass filter of 8-13Hz, normalize real-time power values of the alpha wave frequency band (8-13 Hz) and then use the normalized values as weights of the electroencephalogram signals corresponding to the positions of all the electrodes of the top lobe region, and perform weighted average processing to obtain alpha wave signals with enhanced average calculation.
And the gamma wave oscillation signal separation unit 23 is used for separating the gamma wave oscillation signal from the electroencephalogram signal in the frontal lobe area by combining the fast independent component analysis algorithm with the short-time Fourier transform.
The gamma wave oscillation signal is a fast oscillation signal, and can be generally found in the conscious perception process, compared with other slow brain wave signals, the gamma wave is underestimated due to the fact that the amplitude of the gamma wave is small and is seriously polluted by muscle artifacts, and the time sequence activity of the gamma wave is related to consciousness intensity. The amplitude of the gamma wave oscillation signal is weak, and the forehead myoelectricity artifact (20-300 Hz) and the gamma wave oscillation signal (30-90 Hz) are highly overlapped in the frequency domain, so that the distinction is needed in the extraction process.
Based on the above analysis, in some embodiments of the present invention, the γ -wave oscillation signal separation unit 23 is configured to:
Firstly, the electroencephalogram signal in the forehead lobe area is subjected to component separation for a fixed number of times (10 iterations are simplified) by using a rapid independent component analysis algorithm (FASTICA algorithm), a plurality of independent signal sources are obtained, and only an initial separation result is adopted to ensure real-time performance. In addition, since the consciousness of the emergency patient may be frequently changed, the emergency patient is awake and unconscious, and the peak value of the γ -wave oscillation signal is weak, and may appear piecewise in time sequence, the component signal of each layer in the electroencephalogram signal of the frontal lobe region is converted into a time domain signal by using Short Time Fourier Transform (STFT), and the short time window length may be set to l=100 ms. Combining fastifica and STFT, separate signal components for signal segments in each short time window can be obtained.
Then, since the α -wave signal and the γ -wave oscillation signal may alternately appear in the brain electrical signal of the patient. Thus, by obtaining the energy duty cycle of the alpha wave signal in each short time window of each independent component signal, and the energy duty cycle of the gamma wave signal, and analyzing the pearson correlation coefficient of the alpha wave signal energy duty cycle and the gamma wave signal energy duty cycle of each independent component signal in all short time windows, extracting the spectrum slope of each independent component signal in all short time windows simultaneously (gamma wave oscillation signal exhibits flat characteristics, i.e. signal slope is close to 0, and myoelectric artifact slope is less than-1.5), setting a screening function:
in the formula, Representing a screening function value; Pearson correlation coefficients representing the energy duty cycle of the alpha wave signal and the energy duty cycle of the gamma wave oscillation signal of the independent component signals in all short time windows; Representing the average spectral slope of the individual component signals over all short time windows.
Selecting a screening function valueThe least-squares-corresponding independent component signal, i.e. pearson correlation coefficient of energy ratio of alpha wave signal to gamma wave oscillating signalClosest to-1 (negative correlation), and the average spectral slope of the independent component signalAn independent signal component closest to 0 is used as the target gamma wave oscillation signal.
Finally, the amplitudes of the outputted theta-rhythm signal, alpha-wave signal and gamma-wave oscillating signal are all scaled to the range of [ -1,1], so that the influence of the dimension on the subsequent analysis is avoided.
The phase-amplitude coupling event time position locating module 30 is configured to analyze the coupling probability and the coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window, and obtain an effective phase-amplitude coupling event (effective PAC event) and an occurrence time position thereof.
The θ rhythm signal is a low frequency oscillation from the middle thalamus region, resembling the background beat of human brain activity, and the γ wave oscillation signal is a high frequency oscillation from the frontal lobe region, resembling the consciousness of human brain's rapid flickering. The analysis of the modulation of gamma amplitude by theta phase is essentially to analyze how the thalamo theta beat controls the intensity of the prefrontal gamma flicker.
Therefore, in the embodiment of the present application, the phase-amplitude coupling event time position locating module 30 is configured to analyze the coupling probability and the coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window, so as to obtain the effective phase-amplitude coupling event (effective PAC event) and the occurrence time position thereof. Further, the phase-amplitude coupling event time position locating module 30 includes a signal preprocessing unit 31, a coupling probability analysis unit 32, a coupling direction analysis unit 33, and an effective phase-amplitude coupling event locating unit 34. Wherein:
The signal preprocessing unit 31 is configured to perform short-time fourier transform on the θ rhythm signal and the γ wave oscillation signal, where a longer time window (l=500 ms, including about 2-3 θ wave periods) is used for the θ rhythm signal, the frequency resolution is improved, the window overlapping is set to 75%, for example, the window step length of 500ms is 125ms, information loss is avoided, both ends of the signal are smoothed through hanning windows, and spectrum ambiguity is suppressed, and a shorter time window (l=100 ms) is used for the γ wave oscillation signal to capture a fast transient characteristic, where the time window overlapping is set to 50% and the step length is 50ms.
And a coupling probability analysis unit 32, configured to analyze the divergence of the distribution of the gamma wave oscillation signals in the phase intervals divided according to the θ rhythm signals in each time window, and obtain the coupling probability of the θ rhythm signals to the gamma wave oscillation signals in each time window. Specifically configured to:
The method comprises the steps of firstly, carrying out Hilbert transform (converting into complex form, facilitating phase extraction) on the theta rhythm signals in each time window, extracting all instantaneous phases of the theta rhythm signals in each time window, and eliminating jump points by using a phase unwrapping technology, namely, by detecting phase jump of adjacent sampling points, when the phase change value is larger than 180 degrees, automatically adding and subtracting 360 degrees to enable the adjacent sampling points to be continuous, eliminating 2 pi discontinuity, and obtaining all corrected instantaneous phases of the theta rhythm signals in each time window like a clock from 11 points 59 minutes 23 seconds to 12 points.
Then, the instantaneous energy value of the θ rhythm signal is calculated, and the weight of each instantaneous phase is obtained. Specifically, the instantaneous energy value of the theta rhythm signal is calculated, namely, the square of the amplitude of the theta rhythm signal is calculated and used for reflecting the activity degree of the rhythm, and all the instantaneous energy values in each time window are normalized and then used as the weight of each instantaneous phase in the time window, and because the high-energy period can be an effective modulation event, the theta phase of the high-energy period is given higher weight, and the noise interference in the low-signal-to-noise ratio interval can be restrained.
Then, since the amplitude intensity of the gamma wave oscillation signal reflects the rapid synchronization activity of cortical neurons, the integral energy can quantify the response intensity of the gamma wave oscillation signal in a specific theta phase interval, and the neural mechanism of the gamma wave oscillation signal for theta phase gating is revealed. Therefore, all phases of the θ rhythm signal are divided into several phase sections, specifically, all phases of the θ rhythm signal are equally divided into 18 sections, i.e., each phase section is 20 °.
Then, in an ideal case, the γ amplitude will be significantly increased in some phase intervals, for example, γ is stronger at the θ peak, so that the degree of deviation from uniformity of the distribution is measured by analyzing the dispersion, specifically KL dispersion, of the γ wave oscillation signal distribution in the phase interval in which each instantaneous phase is located.
And finally, traversing all instantaneous phases in each time window according to the weight and the divergence to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window. Specifically, the weight corresponding to all the instantaneous phases in each time window and the KL divergence of the gamma wave oscillation signal distribution in the phase interval where each instantaneous phase is located are weighted and averaged, namely:
in the formula, Representing the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in the time window; Indicating the first time window A plurality of instantaneous phases; representing the number of all instantaneous phases within a time window; Indicating the first time window Weights for the individual instantaneous phases; indicating the first time window KL divergence of the gamma wave oscillation signal distribution in the phase interval where the instantaneous phases are located.
And a coupling direction analysis unit 33, configured to analyze the change sequence of the change of the θ rhythm signal and the change of the γ wave oscillation signal in each time window, and obtain the coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window. Specifically, a calculation formula of the degree of mutual correlation between a theta rhythm signal and a gamma wave oscillation signal in a time window is constructed:
in the formula, Indicating the degree of cross-correlation; representing the time of the position of the mesothalamus region Amplitude of the theta rhythm signal at that time; Representing an iteration delay value; representing forehead lobe area time Amplitude of gamma wave oscillating signal; The variance representing the amplitude of the theta rhythm signal acts to eliminate dimension; the variance of the amplitude of the gamma wave oscillation signal is represented, and the function is to eliminate the dimension; respectively representing the starting time and the ending time of patient monitoring; the expression constant may be 0.0001 to prevent the denominator from being 0.
Representing cross-correlation function, namely giving a delay value to the gamma wave oscillating signal, when the integral value of the amplitude multiplication of the two signals is maximum, representing cross-correlation of the two signals, wherein the delay valueI.e. the information transfer delay between the two signals. Iterative delay valueThe iteration range is (-100 ms to +100 ms), and the acquisition can enableOne delay value corresponding to the maximum valueWhen it is greater than 0, the θ rhythm signal representing the θ rhythm signal is previously changed, and it is considered that there is forward conduction, that is, the θ rhythm signal of the middle thalamus region drives the γ wave oscillation signal of the forehead lobe region to be active.
An effective phase-amplitude coupling event positioning unit 34, configured to obtain an effective phase-amplitude coupling event (effective PAC event) and an occurrence time position thereof according to the coupling probability and the coupling direction. Specifically, the effective phase-amplitude coupled event localization unit 34 is configured to:
Firstly, a coupling probability threshold value is preset, and the value can be 0.25. When the coupling probability is greater than 0.25, it represents that there may be a phase amplitude coupling event (PAC event) within the time window.
Then, judging whether the coupling probability corresponding to each time window is larger than the coupling probability threshold value, and judging whether the coupling direction corresponding to each time window is forward conduction, namely, the theta rhythm signal is changed before the gamma wave oscillation signal is changed, namelyCorresponding time delay value when maximum valueIf it is greater than 0, if it is simultaneously satisfied, thenAnd triggering the coupling judgment if the coupling judgment is forward conduction, namely, the current time window is a time window with a phase amplitude coupling event (PAC event), recording the current time window as a coupling time window, and acquiring all the coupling time windows with the phase amplitude coupling event (PAC event).
And finally, calculating the standard deviation of the amplitude of the gamma wave oscillation signal in a coupling time window, recording the standard deviation as coupling intensity, presetting a coupling intensity base line, specifically presetting the standard deviation of the electroencephalogram signal in a normal person resting state as the coupling intensity base line, and marking the time period corresponding to the continuous coupling event window as the time period with the effective phase amplitude coupling event (effective PAC event) if the coupling intensity corresponding to the continuous 3 coupling event windows exceeds the coupling intensity base line by 2 times.
By extracting the theta, alpha and gamma waves and coupling analysis of the theta-gamma waves, the time positions of independent effective phase and amplitude coupling events (effective PAC events) can be segmented in time sequence, and the problem of insufficient positioning of the effective phase and amplitude coupling events (effective PAC events) in the prior art is solved.
The super sampling module 40 is configured to initiate a super sampling mode (2000 Hz/500 ms) for a period of time in which an active phase amplitude coupling event is present, and capture full band neural oscillation details. Specifically, the sampling rate can be increased from 5000Hz to 2000Hz by linear interpolation, i.e., linear interpolation (adjacent point connection) of the original signal within 250ms before and after the trigger time of each effective phase amplitude coupling event (effective PAC event). The time point density of transient events is increased through interpolation, so that detail features can be conveniently captured.
The patient consciousness state monitoring module 50 is configured to analyze the occurrence density and duration of the effective phase-amplitude coupling event, and the correlation between the α -wave signal and the γ -wave oscillation signal, obtain a patient consciousness state monitoring index, and fit the patient consciousness state monitoring index to the time sequence, so as to complete patient consciousness state monitoring. Further, the patient awareness status monitoring module 50 is configured to:
First, the number of occurrences of the effective phase amplitude coupling event (effective PAC event) per unit time (for example, within one minute) is counted, and the number is recorded as the occurrence density of the effective phase amplitude coupling event (effective PAC event), reflecting the nerve information transfer efficiency, and the awake state is generally higher in density.
Then, the average duration of all the effective phase amplitude coupling events (effective PAC events) in a unit time is calculated and is recorded as the duration of the effective phase amplitude coupling event (effective PAC event), and the long duration may correspond to a stable consciousness state.
And analyzing the correlation of the alpha wave signal and the gamma wave oscillation signal to obtain the change trend of the consciousness state of the patient. Specifically, in a unit time, the correlation attenuation of the alpha wave signal and the gamma wave oscillating signal in the time interval corresponding to all the effective phase amplitude coupling events (effective PAC events) is calculated, namely, the pearson correlation coefficients of the alpha wave signal and the gamma wave oscillating signal in the time interval corresponding to each effective PAC event are calculated, and then the pearson correlation coefficients of the continuous effective PAC events in the unit time are derived to obtain an average derivative value, so that the change trend of the consciousness state of the patient is obtained. Since the alpha wave signal and the gamma wave oscillation signal are inversely related, a larger average derivative represents a tendency of the patient's state of consciousness to fade over time.
And obtaining a time attenuation factor according to the number of all unit time from the monitoring start to the current unit time.
The obtained occurrence density, duration, and trend of change in the consciousness state of the patient, and time attenuation factor are normalized, respectively.
Then, combining the occurrence density, the duration and the change trend of the patient consciousness state and the time attenuation factor to obtain the patient consciousness state monitoring index as follows:
in the formula, Represent the firstMonitoring indexes of the consciousness state of the patient in unit time; indicating from the beginning of monitoring to the th The number of all past unit times per unit time; Representing the current unit time; Represent the first A unit time of passing; Represent the first Average duration of all active phase amplitude coupling events per unit time; Represent the first The occurrence density of effective phase amplitude coupling events per unit time; Represent the first Trend of patient consciousness state change in unit time; a constant is represented, and the value of the constant can be 0.0001 in order to prevent the denominator from being 0; expressed in natural constant An exponential function of the base.
Representing a time decay factor, assigning a higher weight to a recently valid PAC event; The larger the middle numerator, the smaller the denominator, representing the conscious state of the patient, the more conscious, and conversely, the more ambiguous.
Finally, the consciousness state monitoring indexes of the patient are fitted with the time sequence, so that the consciousness state change condition of the emergency patient in the rescue period can be effectively observed, the consciousness state monitoring of the patient is completed, and the problem that the identification of the effective PAC event in the time sequence position is insufficient in the prior art is solved.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. An emergency patient consciousness state monitoring system based on bioelectric signals, the system comprising:
the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals of a thalamus region, a parietal lobe region and a forehead lobe region;
The signal extraction module is used for extracting theta rhythm signals from the brain electrical signals of the middle thalamus region, extracting alpha wave signals from the brain electrical signals of the top lobe region and separating gamma wave oscillation signals from the brain electrical signals of the forehead lobe region;
The phase amplitude coupling event time position positioning module is used for analyzing the coupling probability and the coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window and acquiring an effective phase amplitude coupling event and the occurrence time position thereof;
And the patient consciousness state monitoring module is used for analyzing the occurrence density and the duration of the effective phase amplitude coupling event, analyzing the correlation between the alpha wave signal and the gamma wave oscillation signal, obtaining a patient consciousness state monitoring index, fitting the patient consciousness state monitoring index with a time sequence, and completing patient consciousness state monitoring.
2. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 1, wherein the signal extraction module comprises:
The theta rhythm signal extraction unit is used for enhancing the theta rhythm contribution of the middle thalamus region according to the real-time power of the theta frequency band of the brain electrical signal of the middle thalamus region, and then extracting a low-frequency oscillation component through a 4-7Hz band-pass filter to obtain a theta rhythm signal;
The alpha wave signal extraction unit is used for extracting an alpha wave signal from the brain electrical signal of the top leaf area through a band-pass filter of 8-13Hz and enhancing the alpha wave signal according to the alpha frequency band real-time power;
And the gamma wave oscillation signal separation unit is used for separating the gamma wave oscillation signal from the electroencephalogram signal in the frontal lobe area by combining a rapid independent component analysis algorithm and short-time Fourier transformation.
3. The bioelectric signal based emergency patient consciousness state monitoring system according to claim 2, wherein the gamma wave oscillation signal separation unit is configured to:
Carrying out component separation on the electroencephalogram signals of the forehead lobe area for a fixed number of times by utilizing a rapid independent component analysis algorithm, and carrying out frequency domain conversion on each layer of component signals in the electroencephalogram signals of the forehead lobe area to time domain signals by utilizing short-time Fourier transformation to obtain independent signal components of signal segments in each short time window;
And analyzing the pearson correlation coefficient of the energy ratio of the alpha wave signal and the energy ratio of the gamma wave oscillation signal of each independent component signal in all short time windows, and extracting the frequency gradient of each independent component signal in all short time windows to obtain the gamma wave oscillation signal.
4. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 1, wherein the phase-amplitude coupled event time position location module includes:
The signal preprocessing unit is used for respectively carrying out short-time Fourier transform on the theta rhythm signal and the gamma wave oscillation signal;
the coupling probability analysis unit is used for analyzing the dispersion of the gamma wave oscillation signal distribution in the phase interval divided according to the theta rhythm signal in each time window to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window;
The coupling direction analysis unit is used for analyzing the change sequence of the change of the theta rhythm signal and the change of the gamma wave oscillation signal in each time window to obtain the coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window;
And the effective phase-amplitude coupling event positioning unit is used for acquiring an effective phase-amplitude coupling event and the occurrence time position thereof according to the coupling probability and the coupling direction.
5. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 4, wherein the coupling probability analysis unit is configured to:
extracting all instantaneous phases of the theta rhythm signal in each time window;
calculating the instantaneous energy value of the theta rhythm signal to obtain the weight of each instantaneous phase;
dividing all phases of the theta rhythm signal into a plurality of phase intervals;
analyzing the dispersion of the gamma wave oscillation signal distribution in the phase interval where each instantaneous phase is located;
And traversing all instantaneous phases in each time window according to the weight and the divergence to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window.
6. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 5, wherein the coupling probability analysis unit is further configured to extract all instantaneous phases of the θ rhythm signal within each time window, further comprising before:
performing Hilbert transformation on the theta rhythm signal in each time window, and extracting all instantaneous phases of the theta rhythm signal in each time window, wherein the method further comprises the following steps:
And eliminating the trip point by using a phase unwrapping technology to obtain all corrected instantaneous phases of the theta rhythm signal in each time window.
7. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 4, wherein the effective phase-amplitude coupled event localization unit is configured to:
presetting a coupling probability threshold;
Judging whether the coupling probability corresponding to each time window is larger than the coupling probability threshold value or not, and judging whether the coupling direction corresponding to each time window is that the theta-rhythm signal changes before the gamma-wave oscillation signal changes or not;
if the phase and amplitude coupling events are satisfied at the same time, the time window is a time window with the phase and amplitude coupling events, and the time window is recorded as a coupling time window;
Calculating the standard deviation of the amplitude of the gamma wave oscillation signal in the coupling time window, and marking the standard deviation as the coupling strength;
Presetting a coupling strength base line;
If the coupling strengths corresponding to the continuous 3 coupling event windows exceed the coupling strength baseline by 2 times, the time periods corresponding to the continuous coupling event windows are marked as the time periods with effective phase amplitude coupling events.
8. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 1, further comprising:
And the super sampling module is used for starting a super sampling mode for a time period with an effective phase and amplitude coupling event and capturing full-band nerve oscillation details.
9. The bioelectrical signal based emergency patient consciousness state monitoring system according to claim 1, wherein the patient consciousness state monitoring module is configured to:
Counting the occurrence times of the effective phase-amplitude coupling event in unit time, and recording the occurrence times as the occurrence density of the effective phase-amplitude coupling event;
calculating the average duration of all the effective phase-amplitude coupling events in unit time, and recording the average duration as the duration of the effective phase-amplitude coupling events;
analyzing the correlation of the alpha wave signal and the gamma wave oscillation signal to obtain the change trend of the consciousness state of the patient;
obtaining a time attenuation factor according to the number of all unit time from the monitoring start to the current unit time;
Combining the occurrence density, the duration time, the patient consciousness state change trend and the time attenuation factor to obtain a patient consciousness state monitoring index;
fitting the patient consciousness state monitoring index with time sequence to complete patient consciousness state monitoring.
10. The system for monitoring the consciousness state of an emergency patient based on bioelectric signals according to claim 1, wherein the electroencephalogram signal acquisition module is further used for acquiring triaxial accelerometer signals and constructing a respiratory rhythm harmonic model through the triaxial accelerometer signals so as to eliminate 200-800 ms-level micro-motion artifacts of the electroencephalogram signals.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510933825.7A CN120419978B (en) | 2025-07-08 | 2025-07-08 | Emergency patient consciousness state monitoring system based on bioelectric signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510933825.7A CN120419978B (en) | 2025-07-08 | 2025-07-08 | Emergency patient consciousness state monitoring system based on bioelectric signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN120419978A CN120419978A (en) | 2025-08-05 |
| CN120419978B true CN120419978B (en) | 2025-08-29 |
Family
ID=96552346
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510933825.7A Active CN120419978B (en) | 2025-07-08 | 2025-07-08 | Emergency patient consciousness state monitoring system based on bioelectric signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN120419978B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112867441A (en) * | 2018-07-16 | 2021-05-28 | 通用医疗公司 | System and method for monitoring neural signals |
| CN120217055A (en) * | 2025-03-25 | 2025-06-27 | 重庆邮电大学 | An EEG classification method integrating phase-amplitude coupling features |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2001268332A1 (en) * | 2000-06-07 | 2001-12-17 | New York University | Diagnosis and treatment of thalamocortical dysrhythmia |
| EP2789293A1 (en) * | 2013-04-12 | 2014-10-15 | Commissariat à l'Énergie Atomique et aux Énergies Alternatives | Methods to monitor consciousness |
| CN113576494B (en) * | 2021-07-28 | 2022-07-29 | 生物岛实验室 | EEG signal processing method, device and computer-readable storage medium |
-
2025
- 2025-07-08 CN CN202510933825.7A patent/CN120419978B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112867441A (en) * | 2018-07-16 | 2021-05-28 | 通用医疗公司 | System and method for monitoring neural signals |
| CN120217055A (en) * | 2025-03-25 | 2025-06-27 | 重庆邮电大学 | An EEG classification method integrating phase-amplitude coupling features |
Also Published As
| Publication number | Publication date |
|---|---|
| CN120419978A (en) | 2025-08-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Vaid et al. | EEG signal analysis for BCI interface: A review | |
| Heyat et al. | Sleep bruxism detection using decision tree method by the combination of C4-P4 and C4-A1 channels of scalp EEG | |
| CN103584872B (en) | Psychological stress assessment method based on multi-physiological parameter fusion | |
| CN106691474A (en) | Brain electrical signal and physiological signal fused fatigue detection system | |
| WO2005117693A1 (en) | Patient-specific seizure onset detection system | |
| CN107280663A (en) | A kind of method of the tired brain electrical feature research based on different experiments difficulty | |
| CN105286860A (en) | Motor imagery brain electrical signal recognition method based on dual-tree complex wavelet energy difference | |
| EP4574046A1 (en) | Electroencephalogram signal preprocessing method, system and terminal device | |
| CN106175754A (en) | Waking state detection device in sleep state analysis | |
| Liang et al. | Biosignal processing: principles and practices | |
| Yen et al. | Non-contact estimation of cardiac inter-beat interval and heart rate variability using time-frequency domain analysis for CW radar | |
| Xu et al. | Fatigue EEG feature extraction based on tasks with different physiological states for ubiquitous edge computing | |
| KR101498812B1 (en) | Insomnia tests and derived indicators using eeg | |
| Kuo et al. | Scatterplot analysis of EEG slow-wave magnitude and heart rate variability: an integrative exploration of cerebral cortical and autonomic functions | |
| CN117158970A (en) | An emotion recognition method, system, medium and computer | |
| CN106333676B (en) | Electroencephalogram data type labeling device in waking state | |
| CN120419978B (en) | Emergency patient consciousness state monitoring system based on bioelectric signals | |
| CN119185782A (en) | Non-invasive electrical stimulation regulation and control system and method thereof | |
| Hansson-Sandsten et al. | Multiple window correlation analysis of HRV power and respiratory frequency | |
| Kołodziej et al. | System for automatic heart rate calculation in epileptic seizures | |
| Tass et al. | Complex phase synchronization in neurophysiological data | |
| Mao et al. | Motion artifact reduction in photoplethysmography for reliable signal selection | |
| Ab Rahman et al. | Classification of EEG signal for body earthing application | |
| CN115547490A (en) | Psychological stress prediction method, device, equipment and storage medium | |
| Newandee et al. | Measurement of the electroencephalogram (EEG) coherence in group meditation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |