Emergency patient consciousness monitoring system based on bioelectric signals

Abstract

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

[0001] The present invention relates to the technical field of biological consciousness state monitoring, and in particular to an emergency patient consciousness state monitoring system based on bioelectric signals. Background Art

[0002] The neurophysiological assessment of consciousness in the emergency department mainly relies on spectral quantitative analysis of the electroencephalogram (EEG). For example, the separation of wakefulness and sleep cycles is determined by the power ratio of alpha waves to slow waves or EEG complexity indicators (such as approximate entropy and Lempel-Ziv complexity). Such methods can distinguish between coma and wakefulness with an accuracy of up to 82%, but lack specificity for distinguishing minimally conscious and vegetative states.

[0003] The essence of this limitation lies in the inability of existing methods to resolve the multiscale dynamic characteristics of key neural subsystems associated with consciousness, particularly the dynamic functional connectivity reorganization mechanisms of the cortical-thalamic-brainstem network. Existing techniques are mostly based on Fourier transform or wavelet analysis. To maintain spectral resolution, their temporal localization accuracy cannot capture transient functional connectivity reorganization events in the cortical-thalamic circuit lasting less than 500ms. For example, the cross-band phase-amplitude coupling (PAC) between prefrontal gamma-band oscillations and theta rhythms of the thalamic intralaminar nuclear group lasting 300-400ms. This rapid dynamic coupling has been shown to be a hallmark of residual cognitive function in minimally conscious states, but the intensity of PACs has been significantly underestimated due to issues with temporal localization accuracy. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an emergency patient consciousness status monitoring system based on bioelectric signals.

[0005] According to the embodiment of the present application, the system for monitoring the state of consciousness of emergency patients based on bioelectric signals adopts the following technical solutions:

[0006] EEG signal acquisition module, used to collect EEG signals from the thalamus, parietal lobe, and prefrontal lobe areas;

[0007] a signal extraction module for extracting theta rhythm signals from the EEG signals of the mesothalamus region, extracting alpha wave signals from the EEG signals of the parietal region, and separating gamma wave oscillation signals from the EEG signals of the prefrontal region;

[0008] A phase-amplitude coupling event time position positioning module is used to analyze the coupling probability and coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window, and obtain effective phase-amplitude coupling events and their occurrence time positions;

[0009] The patient consciousness state monitoring module is used to analyze the occurrence density and duration of the effective phase-amplitude coupling events, and analyze the correlation between the α wave signal and the γ wave oscillation signal, to obtain the patient consciousness state monitoring index, and to fit the patient consciousness state monitoring index with the time series to complete the patient consciousness state monitoring.

[0010] In some embodiments of the present invention, the signal extraction module includes:

[0011] a theta rhythm signal extraction unit, configured to enhance the theta rhythm contribution of the mesothalamic region based on the real-time power of the theta frequency band of the EEG signal in the mesothalamic region, and then extract the low-frequency oscillation component through a 4-7 Hz bandpass filter to obtain a theta rhythm signal;

[0012] an alpha wave signal extraction unit, configured to extract alpha wave signals from the EEG signals in the parietal lobe region through an 8-13 Hz bandpass filter and enhance the alpha wave signals according to the real-time power of the alpha frequency band;

[0013] The gamma wave oscillation signal separation unit is used to separate the gamma wave oscillation signal from the EEG signal in the prefrontal lobe area by combining a fast independent component analysis algorithm with a short-time Fourier transform.

[0014] In some embodiments of the present invention, the gamma wave oscillation signal separation unit is configured to:

[0015] The fast independent component analysis algorithm is used to separate the components of the EEG signals in the frontal lobe area for a fixed number of times, and the short-time Fourier transform is used to convert the frequency domain of each layer of the EEG signals in the frontal lobe area into a time domain signal to obtain the independent signal components of the signal segment in each short time window;

[0016] The Pearson correlation coefficient of the energy proportion of the α wave signal and the energy proportion of the γ wave oscillation signal of each independent component signal in all short time windows was analyzed, and the spectrum slope of each independent component signal in all short time windows was extracted to obtain the γ wave oscillation signal.

[0017] In some embodiments of the present invention, the phase-amplitude coupling event time position positioning module includes:

[0018] A signal pre-processing unit is used to perform short-time Fourier transform on the theta rhythm signal and the gamma wave oscillation signal respectively;

[0019] a coupling probability analysis unit, configured to analyze the divergence of the distribution of the gamma wave oscillation signal in the phase intervals divided according to the theta rhythm signal in each time window, and obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window;

[0020] a coupling direction analysis unit, configured to analyze a change sequence of the theta rhythm signal and the gamma wave oscillation signal in each time window, and obtain a coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window;

[0021] The effective phase-amplitude coupling event locating unit is used to obtain the effective phase-amplitude coupling event and its occurrence time position according to the coupling probability and the coupling direction.

[0022] In some embodiments of the present invention, the coupling probability analysis unit is configured to:

[0023] Extracting all instantaneous phases of the theta rhythm signal within each time window;

[0024] Calculating the instantaneous energy value of the θ rhythm signal to obtain the weight of each instantaneous phase;

[0025] Dividing all phases of the θ rhythm signal into a plurality of phase intervals;

[0026] Analyzing the divergence of the gamma wave oscillation signal distribution in the phase interval where each instantaneous phase is located;

[0027] According to the weight and the divergence, all instantaneous phases in each time window are traversed to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window.

[0028] In some embodiments of the present invention, the coupling probability analysis unit is further configured to extract all instantaneous phases of the theta rhythm signal in each time window, which also includes:

[0029] Performing Hilbert transform on the theta rhythm signal in each time window to extract all instantaneous phases of the theta rhythm signal in each time window; and then further comprising:

[0030] The phase unwrapping technique is used to eliminate the jump points and obtain all the corrected instantaneous phases of the θ rhythm signal in each time window.

[0031] In some embodiments of the present invention, the effective phase-amplitude coupling event location unit is configured to:

[0032] Preset coupling probability threshold;

[0033] Determining whether the coupling probability corresponding to each time window is greater than the coupling probability threshold, and determining whether the coupling direction corresponding to each time window is that the change in the theta rhythm signal precedes the change in the gamma wave oscillation signal;

[0034] If both conditions are met, the time window is a time window in which a phase-amplitude coupling event occurs, which is recorded as a coupling time window.

[0035] Calculate the standard deviation of the amplitude of the γ-wave oscillation signal within the coupling time window and record it as the coupling strength;

[0036] Preset coupling strength baseline;

[0037] If the coupling strength corresponding to three consecutive coupling event windows exceeds twice the coupling strength baseline, the time period corresponding to the consecutive coupling event windows is marked as the time period with effective phase-amplitude coupling events.

[0038] In some embodiments of the present invention, the system further comprises:

[0039] The oversampling module is used to start the oversampling mode for the time period when there are effective phase-amplitude coupling events, so as to capture the details of neural oscillations in the full frequency band.

[0040] In some embodiments of the present invention, the patient consciousness status monitoring module is configured to:

[0041] Counting the number of occurrences of the effective phase-amplitude coupling event per unit time, and recording it as the occurrence density of the effective phase-amplitude coupling event;

[0042] Calculate the average duration of all the effective phase-amplitude coupling events within a unit time, and record it as the duration of the effective phase-amplitude coupling event;

[0043] Analyzing the correlation between the α wave signal and the γ wave oscillation signal to obtain a trend of changes in the patient's state of consciousness;

[0044] According to the number of all unit times from the start of monitoring to the current unit time, the time decay factor is obtained;

[0045] Combining the occurrence density, the duration, the trend of the patient's consciousness state change, and the time decay factor, to obtain a patient consciousness state monitoring indicator;

[0046] The patient consciousness state monitoring indicator is fitted with the time series to complete the patient consciousness state monitoring.

[0047] In some embodiments of the present invention, the EEG signal acquisition module is also used to acquire three-axis accelerometer signals, and construct a respiratory rhythm harmonic model through the three-axis accelerometer signals to eliminate 200-800ms level micro-motion artifacts of the EEG signal.

[0048] Compared with the existing technology, the emergency patient consciousness status monitoring system based on bioelectric signals provided by the present invention has the following beneficial effects:

[0049] The present invention collects, extracts and directionally enhances the θ rhythm signal of the thalamus area, the α wave signal of the parietal area and the γ wave oscillation signal of the EEG signal of the prefrontal area by setting an EEG signal acquisition module and a signal extraction module; and analyzes the coupling probability and coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window through the phase amplitude coupling event time position positioning module, and obtains effective phase amplitude coupling events and their occurrence time positions; and based on the patient consciousness state monitoring module, analyzes the occurrence density and duration of effective phase amplitude coupling events, as well as the correlation between the α wave signal and the γ wave oscillation signal, to obtain the patient consciousness state monitoring index, and fits the patient consciousness state monitoring index with the time series to complete the patient consciousness state monitoring. The present invention introduces dynamic time-frequency coupling analysis, including the coordination of long and short time windows, the divergence assessment of gamma waves at specific phases of theta waves, and phase leading detection, to accurately locate theta-gamma cross-brain region phase amplitude coupling (PAC) events; by constructing a temporal attenuation weighted index that integrates PAC event density, duration, and α-gamma correlation, it breaks through the limitations of traditional technologies in the fuzzy spatiotemporal positioning and dynamic tracking lag of PAC events, and realizes millisecond-level high-resolution monitoring of the consciousness state of emergency patients, providing a quantifiable neuroelectrophysiological basis for real-time intervention in consciousness disorders. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A schematic diagram of the basic components of an emergency patient consciousness status monitoring system based on bioelectric signals provided by one embodiment of the present invention;

[0052] Figure 2 A schematic diagram of an electroencephalogram signal provided by one embodiment of the present invention;

[0053] Figure 3 A schematic diagram of EEG electrode distribution provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features and effects of the emergency patient consciousness status monitoring system based on bioelectric signals proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. Terms such as "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a circuit structure, article, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such article or device. In the absence of further limitations, the phrase "comprising a ..." to define an element does not preclude the presence of other identical elements in the article or device comprising the element.

[0056] The following describes in detail a specific scheme of a system for monitoring the state of consciousness of emergency patients based on bioelectric signals provided by the present invention in conjunction with the accompanying drawings.

[0057] See also Figure 1 , which shows the basic composition of an emergency patient consciousness status monitoring system based on bioelectric signals provided by an embodiment of the present invention.

[0058] like Figure 1 As shown, an embodiment of the present invention provides an emergency patient consciousness state monitoring system based on bioelectric signals, which mainly includes an EEG signal acquisition module 10, a signal extraction module 20, a phase amplitude coupling event time position positioning module 30, an oversampling module 40 and a patient consciousness state monitoring module 50. Among them:

[0059] The EEG signal acquisition module 10 is used to collect EEG signals from the thalamus, parietal lobe, and prefrontal lobe regions. Furthermore, the EEG signal acquisition module 10 includes an EEG electrode cap 11 and a data storage unit 12. Specifically, the EEG signal acquisition operation method is as follows: adjust the emergency patient to a semi-recumbent position (with the backrest tilted 45°), and customize the grooves in the headrest to reduce micro-movements of the neck; wear the EEG electrode cap 11 for the patient to ensure the coverage density of the EEG electrodes in the prefrontal lobe region and the central parietal region, and use conductive paste to reduce impedance; use a 256-channel high-density EEG electrode cap 11 (sampling rate ≥ 5000Hz), focusing on enhancing the coverage density of the EEG electrodes in the prefrontal lobe region and the central parietal region.

[0060] 256-channel high-density EEG refers to a system that uses 256 electrodes to simultaneously record brain electrical activity. Each electrode can independently collect the EEG signal at its corresponding position. Figure 2 This is a schematic diagram of EEG signals. From the 256 channels, the EEG signals collected by electrodes located in the three major areas of the thalamus, parietal lobe, and prefrontal lobe are screened out. Figure 3 Figure 1 is a schematic diagram of electrode distribution, where the mesothalamus may include: C3, Cz, C4, O1, O2; the parietal region may include: T3, T4, T5, T6, P3, P4, Pz, CP1, CP2; and the prefrontal region may include: F7, F3, Fz, F4, F8, FC1, FC2, Fp1, Fp2. EEG signals are acquired in real time from 256 electrode locations and stored in data storage unit 12 in chronological order corresponding to the signal acquisition locations.

[0061] In some embodiments of the present invention, the EEG signal acquisition module 10 further includes a three-axis accelerometer 13, which is fixed to the chest, the baseline motion signal is calibrated, and the signal of the three-axis accelerometer 13 is collected; and the EEG signal acquisition module 10 is configured to construct a respiratory rhythm harmonic model through the signal of the three-axis accelerometer 13, eliminate the 200-800ms level micro-motion artifacts of all collected EEG signals, and obtain denoised EEG signals. The known technology will not be described in detail. The obtained denoised EEG signals are stored in the data storage unit 12 according to the time sequence corresponding to the signal acquisition position. It should be noted that, unless otherwise specified, the EEG signals used subsequently are all denoised EEG signals.

[0062] The signal extraction module 20 is used to extract the theta rhythm signal from the EEG signal of the mesothalamus area, extract the alpha wave signal from the EEG signal of the parietal lobe area, and separate the gamma wave oscillation signal from the EEG signal of the prefrontal lobe area.

[0063] When consciousness emerges, the theta wave phase of the sublaminar nuclei in the mesothalamic region is first synchronized, and through cross-frequency coupling (locking of the theta wave phase with the gamma wave amplitude in the prefrontal region), it drives the gamma wave oscillation signal (60-150 Hz) in the lateral prefrontal region. This coupling has a clear directionality (mesothalamic region → prefrontal region). The theta rhythm signal of the sublaminar nuclei in the mesothalamic region refers to the neural oscillation activity in the theta wave frequency band (4-7 Hz) participating in the sublaminar nuclei in the mesothalamic region and their adjacent medial nuclei. Its core function is to integrate cortical activity through cross-brain phase synchronization mechanisms to form a coherent conscious experience and memory processing.

[0064] In normal adults, when relaxed, especially when not mentally active, alpha waves (8-13 Hz) can be observed spontaneously. They are particularly prominent in the parietal lobe when eyes are closed.

[0065] Based on the above analysis, in an embodiment of the present invention, a signal extraction module 20 is provided to extract the theta rhythm signal from the EEG signal of the mesothalamus region, extract the alpha wave signal from the EEG signal of the parietal lobe region, and separate the gamma wave oscillation signal from the EEG signal of the prefrontal lobe region, as basic data for analyzing the state of consciousness of emergency patients. Furthermore, the signal extraction module 20 includes a theta rhythm signal extraction unit 21, an alpha wave signal extraction unit 22, and a gamma wave oscillation signal separation unit 23. Among them:

[0066] The theta rhythm signal extraction unit 21 is configured to enhance the theta rhythm contribution of the mesothalamic region based on the real-time power of the theta frequency band of the EEG signal in the mesothalamic region, and then extract the low-frequency oscillation component through a 4-7 Hz bandpass filter to obtain the theta rhythm signal. Specifically, the unit is configured to perform mean filtering on all EEG signals in the mesothalamic region, and then normalize the real-time power value of the theta frequency band (4-8 Hz) of the EEG signal collected from each electrode in the mesothalamic region according to the standard brain atlas, and use this power value as the weight of the EEG signal corresponding to each electrode position. The weighted summation of the filtered EEG signals of each channel is used to directly enhance the contribution of the theta rhythm signal in the mesothalamic region. A 4-7 Hz bandpass filter is then used to extract the low-frequency oscillation component and suppress interference from other frequencies to obtain a time domain signal containing the theta rhythm signal.

[0067] The alpha wave signal extraction unit 22 is configured to extract alpha wave signals from EEG signals in the parietal region using an 8-13 Hz bandpass filter and enhance the alpha wave signals based on the real-time power of the alpha frequency band. Specifically, the unit is configured to directly extract alpha wave signals from all EEG signals in the parietal region using an 8-13 Hz bandpass filter. The real-time power values ​​of the alpha wave frequency band (8-13 Hz) are normalized and used as weights for the EEG signals corresponding to each electrode position in the parietal region. Weighted averaging is then performed to obtain the enhanced and averaged alpha wave signals.

[0068] The gamma wave oscillation signal separation unit 23 is used to separate the gamma wave oscillation signal from the EEG signal in the prefrontal lobe area by combining a fast independent component analysis algorithm with a short-time Fourier transform.

[0069] Gamma waves are fast-oscillating signals that are often detected during conscious perception. Compared to other slower EEG signals, gamma waves are underestimated due to their small amplitude and significant contamination by muscle artifacts. Their temporal activity correlates with the strength of consciousness. Gamma waves have a weak amplitude, and forehead EMG artifacts (20-300Hz) and gamma waves (30-90Hz) overlap significantly in the frequency domain, necessitating their differentiation during extraction.

[0070] Based on the above analysis, in some embodiments of the present invention, the gamma wave oscillation signal separation unit 23 is configured as follows:

[0071] First, the fast independent component analysis (FASTICA) algorithm is used to perform component separation on the EEG signals in the prefrontal region for a fixed number of times (simplified to 10 iterations), resulting in several independent signal sources. Only the initial separation results are used to ensure real-time performance. Furthermore, because emergency patients' states of consciousness can fluctuate frequently, with alternating periods of awakening and unconsciousness, and the peaks of gamma wave oscillations are weak and may appear fragmented in time, the short-time Fourier transform (STFT) is used to convert the component signals of each layer of the EEG signals in the prefrontal region from the frequency domain to the time domain. The short time window length can be set to L = 100ms. Combining FASTICA and STFT can obtain independent signal components for each signal segment within each short time window.

[0072] Then, since α-wave signals and γ-wave oscillation signals may appear alternately in the patient's EEG signals, we obtain the energy proportion of the α-wave signal and the energy proportion of the γ-wave oscillation signal in each short-time window of each independent component signal, and analyze the Pearson correlation coefficient of the energy proportion of the α-wave signal and the energy proportion of the γ-wave oscillation signal of each independent component signal in all short-time windows; at the same time, we extract the spectrum slope of each independent component signal in all short-time windows (the γ-wave oscillation signal presents a flat characteristic, that is, the signal slope is close to 0, while the electromyographic artifact slope is less than -1.5); set the screening function:

[0073]

[0074] Where, Represents the filter function value; The Pearson correlation coefficient of the energy proportion of the α wave signal and the energy proportion of the γ wave oscillation signal in all short time windows of the independent component signal; Represents the average spectral slope of the independent component signal in all short time windows.

[0075] Select filter function value The Pearson correlation coefficient of the energy ratio of the α wave signal and the γ wave oscillation signal corresponding to the minimum is The average spectral slope of the independent component signals is closest to -1 (negative correlation) The independent signal component closest to 0 is used as the target gamma wave oscillation signal.

[0076] Finally, the amplitudes of the output θ rhythm signal, α wave signal, and γ wave oscillation signal are all scaled to the range of [-1, 1] to avoid the subsequent analysis being affected by the dimension.

[0077] The phase-amplitude coupling event time position positioning module 30 is used to analyze the coupling probability and coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window, and obtain effective phase-amplitude coupling events (effective PAC events) and their occurrence time positions.

[0078] Theta rhythm signals are low-frequency oscillations originating from the mesothalamus, similar to the background beat of human brain activity. Gamma oscillation signals originate from high-frequency oscillations in the prefrontal cortex, similar to the rapid flickering of consciousness in the human brain. Analyzing the modulation of gamma amplitude by theta phase essentially examines how the thalamic theta beat controls the intensity of gamma flickers in the prefrontal cortex.

[0079] Therefore, in the embodiment of the present application, a phase-amplitude coupling event temporal location module 30 is provided to analyze the coupling probability and coupling direction of the theta rhythm signal to the gamma wave oscillation signal within each time window, thereby obtaining effective phase-amplitude coupling events (effective PAC events) and their occurrence time locations. Furthermore, the phase-amplitude coupling event temporal location module 30 includes a signal pre-processing unit 31, a coupling probability analysis unit 32, a coupling direction analysis unit 33, and an effective phase-amplitude coupling event location unit 34. Specifically:

[0080] The signal pre-processing unit 31 is used to perform short-time Fourier transform on the θ rhythm signal and the γ wave oscillation signal respectively. A longer time window (L=500ms, containing approximately 2-3 θ wave cycles) is used for the θ rhythm signal to improve the frequency resolution. The window overlap is set to 75%, such as a 500ms window with a step size of 125ms to avoid information loss. The two ends of the signal are smoothed through a Hanning window to suppress spectral blur. A short time window (L=100ms) is used for the γ wave oscillation signal to capture fast transient characteristics. The time window overlap is set to 50% with a step size of 50ms.

[0081] The coupling probability analysis unit 32 is used to analyze the divergence of the distribution of the gamma wave oscillation signal in the phase interval divided according to the theta rhythm signal in each time window, and obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window. Specifically, it is configured as follows:

[0082] First, the θ rhythm signal in each time window is Hilbert transformed (converted into complex form to facilitate phase extraction), and all instantaneous phases of the θ rhythm signal in each time window are extracted; and phase unwrapping technology is used to eliminate jump points. That is, by detecting the phase jumps of adjacent sampling points, when the phase change value is greater than 180°, 360° is automatically added or subtracted to make it continuous, eliminating the 2π discontinuity; (similar to the transition of a clock from 11:59:23 directly to 12:00), all corrected instantaneous phases of the θ rhythm signal in each time window are obtained.

[0083] Then, the instantaneous energy of the theta rhythm signal is calculated to obtain the weight of each instantaneous phase. Specifically, the instantaneous energy of the theta rhythm signal is calculated, that is, the square of the theta rhythm signal amplitude is calculated to reflect the activity level of the rhythm. All instantaneous energy values ​​within each time window are normalized and used as the weight of each instantaneous phase within the time window. Because high-energy periods may be effective modulation events, the theta phases in high-energy periods should be given higher weights, which can suppress noise interference in low signal-to-noise ratio intervals.

[0084] Since the amplitude of the gamma wave oscillation signal reflects the rapid synchronization of cortical neurons, the integrated energy can quantify its response strength within a specific theta phase interval, revealing the neural mechanism by which theta phase gates the gamma amplitude. Therefore, all phases of the theta rhythm signal are divided into several phase intervals. Specifically, all phases of the theta rhythm signal are divided into 18 intervals, that is, each phase interval is 20 degrees.

[0085] Then, ideally, the gamma amplitude will increase significantly in certain phase intervals, for example, gamma is stronger at the peak of the theta wave. Therefore, by analyzing the divergence of the gamma wave oscillation signal distribution in the phase interval where each instantaneous phase is located, specifically the KL divergence, the degree to which the distribution deviates from uniformity is measured.

[0086] Finally, based on the weights and divergence, all instantaneous phases in each time window are traversed to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window. Specifically, the weights corresponding to all 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, that is:

[0087]

[0088] Where, represents the coupling probability of the θ rhythm signal to the γ wave oscillation signal within the time window; Indicates the time window instantaneous phase; Represents the number of all instantaneous phases in the time window; Indicates the time window The weight of the instantaneous phase; Indicates the time window The KL divergence of the γ-wave oscillation signal distribution in the phase interval where the instantaneous phase is located.

[0089] The coupling direction analysis unit 33 is used to analyze the change sequence of the θ rhythm signal and the γ wave oscillation signal in each time window to obtain the coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window. Specifically, a calculation formula for the degree of mutual correlation between the θ rhythm signal and the γ wave oscillation signal in the time window is constructed:

[0090]

[0091] Where, Indicates the degree of mutual connection; Indicates the location time of the mesothalamic region The amplitude of the θ rhythm signal at ; Indicates the iteration delay value; Indicates the time of the frontal lobe area The amplitude of the γ wave oscillation signal at ; It represents the variance of the amplitude of the θ rhythm signal, and its function is to eliminate the dimension; It represents the variance of the amplitude of the gamma wave oscillation signal, and its function is to eliminate the dimension; Respectively represent the start and end time of patient monitoring; Indicates a constant. To prevent the denominator from being 0, the value can be 0.0001.

[0092] Represents the cross-correlation function, which is to give the γ wave oscillation signal a time delay value. When the integral value of the product of the amplitudes of the two signals is the largest, it means that the two signals are cross-correlated. At this time, the time delay value It is the information transmission delay between two signals. , the iteration range is (-100ms~+100ms), and the The delay value corresponding to the maximum value When it is greater than 0, it means that the θ rhythm signal changes first, and it is considered that there is positive conduction, that is, the θ rhythm signal in the middle thalamus area drives the γ wave oscillation signal in the prefrontal area to become active.

[0093] The effective phase-amplitude coupling event location unit 34 is used to obtain effective phase-amplitude coupling events (effective PAC events) and their occurrence time locations based on the coupling probability and coupling direction. Specifically, the effective phase-amplitude coupling event location unit 34 is configured to:

[0094] First, a coupling probability threshold is preset, which can be set to 0.25. When the coupling probability is greater than 0.25, it indicates that a phase amplitude coupling event (PAC event) may exist within the time window.

[0095] Then, it is determined whether the coupling probability corresponding to each time window is greater than the coupling probability threshold, and whether the coupling direction corresponding to each time window is forward conduction, that is, the change of the θ rhythm signal precedes the change of the γ wave oscillation signal, that is, The delay value corresponding to the maximum value Is it greater than 0? If both are satisfied, then , and it is forward conduction at the same time, then the coupling judgment is triggered, that is, the current time window is the time window with a phase amplitude coupling event (PAC event), recorded as the coupling time window, and all coupling time windows with phase amplitude coupling events (PAC events) are obtained.

[0096] Finally, the standard deviation of the amplitude of the γ-wave oscillation signal within the coupling time window is calculated and recorded as the coupling strength; and a coupling strength baseline is preset. Specifically, the standard deviation of the EEG signal of a normal person in a resting state is preset as the coupling strength baseline; if the coupling strength corresponding to three consecutive coupling event windows exceeds 2 times the coupling strength baseline, the time period corresponding to the consecutive coupling event windows is marked as a time period with a valid phase-amplitude coupling event (valid PAC event).

[0097] At this point, through the extraction of θ, α, and γ waves and the coupling analysis of θ-γ waves in this application, the time position of independent effective phase-amplitude coupling events (effective PAC events) can be segmented in time sequence, solving the problem of insufficient position positioning of effective phase-amplitude coupling events (effective PAC events) in the prior art.

[0098] Oversampling module 40 is used to activate oversampling mode (2000 Hz / 500 ms) during time periods with significant phase-amplitude coupling events, capturing details of neural oscillations across the entire frequency range. Specifically, linear interpolation is used to increase the sampling rate from 5000 Hz to 2000 Hz by linearly interpolating the original signal (connecting adjacent points) within 250 ms before and after each significant phase-amplitude coupling event (valid PAC event). This interpolation increases the time point density of transient events, facilitating the capture of detailed features.

[0099] The patient consciousness state monitoring module 50 is used to analyze the occurrence density and duration of effective phase-amplitude coupling events, as well as the correlation between the α-wave signal and the γ-wave oscillation signal, to obtain the patient consciousness state monitoring index, and to fit the patient consciousness state monitoring index with the time series to complete the patient consciousness state monitoring. Further, the patient consciousness state monitoring module 50 is configured to:

[0100] First, the number of effective phase-amplitude coupling events (effective PAC events) within a unit time (for example, within one minute) is counted and recorded as the occurrence density of effective phase-amplitude coupling events (effective PAC events), which reflects the efficiency of neural information transmission. The density is usually higher in the awake state.

[0101] Then, the average duration of all effective phase-amplitude coupling events (effective PAC events) per unit time is calculated and recorded as the duration of the effective phase-amplitude coupling event (effective PAC event). A long duration may correspond to a stable state of consciousness.

[0102] Furthermore, the correlation between α-wave and γ-wave oscillation signals is analyzed to determine the trend of changes in the patient's consciousness state. Specifically, the attenuation of the correlation between the α-wave and γ-wave oscillation signals in the time interval corresponding to all valid phase-amplitude coupling events (valid PAC events) per unit time is calculated. Specifically, the Pearson correlation coefficient between the α-wave and γ-wave oscillation signals in the time interval corresponding to each valid PAC event is calculated. The Pearson correlation coefficient of consecutive valid PAC events per unit time is then derived to obtain the average derivative, which provides the trend of changes in the patient's consciousness state. Since the α-wave and γ-wave oscillation signals are negatively correlated, a larger average derivative indicates a trend of weakening of the patient's consciousness over time.

[0103] And, according to the number of all unit times from the start of monitoring to the current unit time, a time attenuation factor is obtained.

[0104] It should be noted that the occurrence density, duration, and changing trend of the patient's consciousness state and time attenuation factor obtained above were standardized to eliminate the dimension.

[0105] Then, combining the occurrence density, duration, and the trend of changes in the patient's consciousness state and the time decay factor, the patient's consciousness state monitoring index is obtained as follows:

[0106]

[0107] Where, Indicates the Patient consciousness status monitoring indicators per unit time; From the start of monitoring to All past unit time quantities up to unit time; Indicates the current unit time; Indicates the past unit time; Indicates the The average duration of all effective phase-amplitude coupling events per unit time; Indicates the The occurrence density of effective phase-amplitude coupling events per unit time; Indicates the The trend of changes in the patient's consciousness status within a unit of time; Indicates a constant. To prevent the denominator from being 0, the value can be 0.0001; Expressed as a natural constant An exponential function with base .

[0108] represents the time decay factor, giving higher weight to recent valid PAC events; The larger the numerator and the smaller the denominator, the clearer the patient's consciousness is, and vice versa.

[0109] Finally, by fitting the patient consciousness status monitoring indicators with the time series, the changes in the consciousness status of emergency patients during the rescue period can be effectively observed, and the patient consciousness status monitoring can be completed, which solves the problem of insufficient recognition of effective PAC events in the time series position by the existing technology.

[0110] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A system for monitoring the state of consciousness of emergency patients based on bioelectric signals, characterized in that: The system comprises: EEG signal acquisition module, used to collect EEG signals from the thalamus, parietal lobe, and prefrontal lobe areas; a signal extraction module for extracting theta rhythm signals from the EEG signals of the mesothalamus region, extracting alpha wave signals from the EEG signals of the parietal region, and separating gamma wave oscillation signals from the EEG signals of the prefrontal region; A phase-amplitude coupling event time position positioning module is used to analyze the coupling probability and coupling direction of the θ rhythm signal to the γ wave oscillation signal in each time window, and obtain effective phase-amplitude coupling events and their occurrence time positions; The patient consciousness state monitoring module is used to analyze the occurrence density and duration of the effective phase-amplitude coupling events, and analyze the correlation between the α wave signal and the γ wave oscillation signal, to obtain the patient consciousness state monitoring index, and to fit the patient consciousness state monitoring index with the time series to complete the patient consciousness state monitoring.

2. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 1, characterized in that: The signal extraction module includes: a theta rhythm signal extraction unit, configured to enhance the theta rhythm contribution of the mesothalamic region based on the real-time power of the theta frequency band of the EEG signal in the mesothalamic region, and then extract the low-frequency oscillation component through a 4-7 Hz bandpass filter to obtain a theta rhythm signal; an alpha wave signal extraction unit, configured to extract alpha wave signals from the EEG signals in the parietal lobe region through an 8-13 Hz bandpass filter and enhance the alpha wave signals according to the real-time power of the alpha frequency band; The gamma wave oscillation signal separation unit is used to separate the gamma wave oscillation signal from the EEG signal in the prefrontal lobe area by combining a fast independent component analysis algorithm with a short-time Fourier transform.

3. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 2, characterized in that: The gamma wave oscillation signal separation unit is configured as follows: The fast independent component analysis algorithm is used to separate the components of the EEG signals in the frontal lobe area for a fixed number of times, and the short-time Fourier transform is used to convert the frequency domain of each layer of the EEG signals in the frontal lobe area into a time domain signal to obtain the independent signal components of the signal segment in each short time window; The Pearson correlation coefficient of the energy proportion of the α wave signal and the energy proportion of the γ wave oscillation signal of each independent component signal in all short time windows was analyzed, and the spectrum slope of each independent component signal in all short time windows was extracted to obtain the γ wave oscillation signal.

4. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 1, characterized in that: The phase-amplitude coupling event time position positioning module includes: A signal pre-processing unit is used to perform short-time Fourier transform on the theta rhythm signal and the gamma wave oscillation signal respectively; a coupling probability analysis unit, configured to analyze the divergence of the distribution of the gamma wave oscillation signal in the phase intervals divided according to the theta rhythm signal in each time window, and obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window; a coupling direction analysis unit, configured to analyze a change sequence of the theta rhythm signal and the gamma wave oscillation signal in each time window, and obtain a coupling direction of the theta rhythm signal to the gamma wave oscillation signal in each time window; The effective phase-amplitude coupling event locating unit is used to obtain the effective phase-amplitude coupling event and its occurrence time position according to the coupling probability and the coupling direction.

5. The emergency patient consciousness status monitoring system based on bioelectric signals according to claim 4 is characterized in that: The coupling probability analysis unit is configured to: Extracting all instantaneous phases of the theta rhythm signal within each time window; Calculating the instantaneous energy value of the θ rhythm signal to obtain the weight of each instantaneous phase; Dividing all phases of the θ rhythm signal into a plurality of phase intervals; Analyzing the divergence of the gamma wave oscillation signal distribution in the phase interval where each instantaneous phase is located; According to the weight and the divergence, all instantaneous phases in each time window are traversed to obtain the coupling probability of the theta rhythm signal to the gamma wave oscillation signal in each time window.

6. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 5, characterized in that: The coupling probability analysis unit is further configured to extract all instantaneous phases of the theta rhythm signal within each time window, and the method also includes: Performing Hilbert transform on the theta rhythm signal in each time window to extract all instantaneous phases of the theta rhythm signal in each time window; and then further comprising: The phase unwrapping technique is used to eliminate the jump points and obtain all the corrected instantaneous phases of the θ rhythm signal in each time window.

7. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 4, characterized in that: The effective phase-amplitude coupling event location unit is configured as follows: Preset coupling probability threshold; Determining whether the coupling probability corresponding to each time window is greater than the coupling probability threshold, and determining whether the coupling direction corresponding to each time window is that the change in the theta rhythm signal precedes the change in the gamma wave oscillation signal; If both conditions are met, the time window is a time window in which a phase-amplitude coupling event occurs, which is recorded as a coupling time window. Calculate the standard deviation of the amplitude of the γ-wave oscillation signal within the coupling time window and record it as the coupling strength; Preset coupling strength baseline; If the coupling strength corresponding to three consecutive coupling event windows exceeds twice the coupling strength baseline, the time period corresponding to the consecutive coupling event windows is marked as the time period with effective phase-amplitude coupling events.

8. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 1, characterized in that: The system further comprises: The oversampling module is used to start the oversampling mode for the time period when there are effective phase-amplitude coupling events, so as to capture the details of neural oscillations in the full frequency band.

9. The emergency patient consciousness monitoring system based on bioelectric signals according to claim 1, characterized in that: The patient consciousness status monitoring module is configured to: Counting the number of occurrences of the effective phase-amplitude coupling event per unit time, and recording it as the occurrence density of the effective phase-amplitude coupling event; Calculate the average duration of all the effective phase-amplitude coupling events within a unit time, and record it as the duration of the effective phase-amplitude coupling event; Analyzing the correlation between the α wave signal and the γ wave oscillation signal to obtain a trend of changes in the patient's state of consciousness; According to the number of all unit times from the start of monitoring to the current unit time, the time decay factor is obtained; Combining the occurrence density, the duration, the trend of the patient's consciousness state change, and the time decay factor, to obtain a patient consciousness state monitoring indicator; The patient consciousness state monitoring indicator is fitted with the time series to complete the patient consciousness state monitoring.

10. The emergency patient consciousness status monitoring system based on bioelectric signals according to claim 1, characterized in that: The EEG signal acquisition module is also used to acquire triaxial accelerometer signals and construct a respiratory rhythm harmonic model through the triaxial accelerometer signals to eliminate 200-800ms-level micro-motion artifacts of the EEG signals.

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