Hand function evaluation method for stroke patient

Abstract

The invention discloses a hand function evaluation method for a stroke patient, and relates to the technical field of bio-electricity signal processing. Decomposing brain and electromyographic signals; extracting phase information by using Hilbert change; estimating a modulation index by adopting phase amplitude coupling; and upper limb rehabilitation grade evaluation. According to the technical scheme, the stroke patient hand rehabilitation evaluation method based on the electroencephalogram and electromyographic signals is provided through empirical mode decomposition and tensor-based phase-amplitude coupling-multi-scale condition mutual information, quantification of the rehabilitation degree is achieved, and the rehabilitation condition of the patient is quantitatively analyzed in combination with a stroke clinical evaluation scale.

Description

Technical Field

[0001] The present invention relates to the technical field of bioelectric signal processing, and in particular to a method for evaluating hand function of stroke patients. Background Art

[0002] Stroke is a brain tissue injury caused by interruption or abnormality of blood supply to the brain, and is usually divided into ischemic stroke (cerebral infarction) and hemorrhagic stroke (cerebral hemorrhage). Stroke not only brings great physical and psychological burden to patients, but also brings huge economic pressure and care challenges to society and families. Clinical data show that the recurrence rate of acute stroke patients is very high, reaching 17.7%, and the five-year cumulative recurrence rate exceeds 30%.

[0003] The rehabilitation process of stroke is complex and long, usually starting after acute treatment and continuing through all stages of functional recovery. The goal of rehabilitation is to restore the patient's function to the greatest extent possible, improve the quality of life, and help the patient live as independently as possible. Despite this, there are still many challenges in the rehabilitation of stroke patients, the most prominent of which include the lack of quantitative means and timely feedback for evaluating the rehabilitation process. Clinically, sleep physicians and rehabilitation physicians rely more on empirical observations to judge the patient's rehabilitation effect, and lack scientific and standardized rehabilitation progress assessment tools. At the same time, the rehabilitation effects of stroke patients with different causes vary greatly, and the rehabilitation progress of some patients is slow or fails to achieve the expected results, which makes overall rehabilitation management particularly complicated.

[0004] Therefore, it is particularly important to develop a portable monitoring system that can effectively evaluate and record the neuromuscular electrical signals of stroke patients during their rehabilitation process. The acquisition and analysis of physiological signals such as electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG) can not only provide real-time monitoring data for the rehabilitation of stroke patients, but also help medical staff to fully understand the physiological status of patients. In particular, the interaction between EEG and EMG signals (such as phase-amplitude coupling, PAC) provides a unique perspective for evaluating the transmission of neural control information between the cerebral cortex and limb muscles.

[0005] Phase-amplitude coupling (PAC), as an important phenomenon in neural information transmission, has a significant enhancement in the state of cognitive functions such as perception, learning and memory. Studies have shown that changes in PAC are closely related to the onset and clinical symptoms of some neurological diseases (such as Parkinson's disease, epilepsy, etc.). For stroke patients, changes in PAC can reflect changes in the control signal transmission between the cerebral cortex and the muscle system, thereby providing a scientific basis for rehabilitation evaluation. Therefore, the evaluation algorithm based on the coupling of EEG and EMG signals is expected to become an effective tool to help doctors accurately evaluate the patient's rehabilitation status, monitor the rehabilitation process in real time, and even provide patients with autonomous rehabilitation means without physician supervision after discharge. Summary of the invention

[0006] The technical solution of the present invention to solve the above technical problem is to provide a method for evaluating hand function of stroke patients, comprising the following steps:

[0007] Step 1, signal acquisition and preprocessing: Combined with the Brunnstrom rating scale, the action paradigm was set, and the synchronous EEG signals and EMG signals of patients with different levels of stroke were collected at the same time, and the EEG signals and EMG signals were preprocessed respectively;

[0008] Step 2, decomposition of brain and electromyographic signals: Synchronously decompose the preprocessed brain and electromyographic signals based on wavelet packet transform, including constructing a wavelet packet function and selecting a wavelet packet basis function, decomposing the original signal, using a multi-resolution decomposition algorithm to calculate the wavelet coefficients of each level, and obtaining the sub-band signals of the original signal in each frequency according to the wavelet coefficients. The wavelet approximation coefficients and the wavelet coefficients are reconstructed using a multi-resolution reconstruction algorithm, and the reconstructed signals are separated according to the active bands of EEG and EMG, and the individual frequency domain signals are saved;

[0009] Step 3, extract phase information using Hilbert transform: convert the decomposed brain and electromyographic signals into time series, use discrete Hilbert transform to convert the time series into complex signals, calculate the instantaneous phase by calculating the phase angle of the complex signal, calculate the instantaneous amplitude, and perform phase verification;

[0010] Step 4: Use phase-amplitude coupling to estimate the modulation index: Cut the phase φ(n) into n slices, and the phase is distributed into n intervals, each interval is 360 / n°; take the average value of the amplitude a(t) in each demarcated interval, and use <a(t) φ Indicates that the probability distribution P is obtained by dividing the amplitude in each interval by the sum of the intervals; the modulation index is obtained using the Kullback-Leibler distance;

[0011] Step 5, assessment of upper limb rehabilitation level: Based on the Spearman correlation coefficient, the fitting relationship between each patient's MI and the Brunnstrom rating scale was mapped, and the FCMC characteristic indicators and change trends that were significantly correlated with the Brunnstrom rating scale level were analyzed to quantitatively assess the disease level of stroke patients.

[0012] Furthermore, in step 1, the EEG signals of the stroke patients at leads Fp1, Fp2, F3, F4, C3, C4, Cz, P3, P4, A1, and A2 are collected respectively, and the EMG signals at the anterior bundle of the deltoid muscle, the middle bundle of the deltoid muscle, the biceps brachii, the triceps brachii, and the extensor digitorum of the forearm are collected.

[0013] The preprocessing operations of EEG signals include: re-referencing, baseline correction, filtering, removal of electrooculogram and EEG data segmentation; the preprocessing operations of EMG signals include: baseline correction, filtering and EMG data segmentation.

[0014] Furthermore, in step 2, the wavelet transform performs multi-resolution analysis on the signal through the scaling function φ(t) and the wavelet function ψ(t); the orthogonality condition is satisfied, and the wavelet basis function is generated by scaling and translating:

[0015] φ j,k (t) = 2 j / 2 φ(2 j tk),ψ j,k (t) = 2 j / 2 ψ(2 j tk);

[0016] Among them, j is the scale parameter and k is the translation parameter;

[0017] At the jth layer, the wavelet packet basis function Defined as:

[0018]

[0019] In the jth layer, the wavelet packet function of the nth node;

[0020] h k (t): low-pass filter coefficient;

[0021] g k (t): high-pass filter coefficient;

[0022] The original signal is gradually disassembled using a decomposition algorithm and compared with the wavelet function. Assuming the original function is F, the original function is decomposed into F1, F2, F3, F4... step by step; and with the wavelet part W1, W2, W3, W4..., F1=FJ-1+WJ-1, decomposing to the 0th level;

[0023] The signal of each layer passes through a low-pass filter and a high-pass filter to calculate the decomposition coefficients:

[0024]

[0025] The signal value of the nth node in the jth layer;

[0026] h[m], g[m]: low-pass and high-pass filter coefficients;

[0027] The wavelet coefficients at each level are calculated using a multiresolution decomposition algorithm;

[0028] According to the wavelet coefficients, the sub-band signals of the original signal in each frequency are obtained;

[0029] The wavelet approximation coefficients and wavelet coefficients are reconstructed using a multi-resolution reconstruction algorithm in the form of discrete filters; the signal is reconstructed using a linear combination of wavelet packet coefficients:

[0030]

[0031] Through inverse filtering and layer-by-layer reconstruction;

[0032]

[0033] Separate the reconstructed signal according to the active bands of EEG and EMG; and decompose the EMG signal into the same frequency bands;

[0034] Save the individual frequency domain signals.

[0035] Furthermore, in step 3, the decomposed brain and myoelectric signals are converted into time series;

[0036] Use discrete Hilbert transform to transform the time series into a complex signal;

[0037]

[0038] The instantaneous phase is obtained by calculating the phase angle of the complex signal;

[0039] φ(t)=arg(z(t));

[0040] The instantaneous amplitude is calculated by the calculation formula;

[0041]

[0042] Compensate between phase differences;

[0043] φ unwrapped (n) = φ unwrapped (n-1)+Δφ(n);

[0044] in:

[0045] Δφ(n)=φ unwrapped (n)-φ unwrapped (n-1);

[0046] In order to ensure phase continuity, it is necessary to adjust;

[0047]

[0048] The final unwrapped phase is:

[0049] φ unwrapped (n) = φ unwrapped (n-1)+Δφ adjusted (n);

[0050] Perform a phase check.

[0051] The technical solution of the present invention proposes a hand rehabilitation assessment method for stroke patients based on brain electromyographic signals through empirical mode decomposition and tensor-based phase-amplitude coupling-multi-scale conditional mutual information, which realizes the quantification of the degree of rehabilitation. Combined with the stroke clinical assessment scale, it has certain significance for quantitative analysis of the patient's rehabilitation status. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 the structures shown in these drawings without paying creative work.

[0053] Figure 1 This is a flow chart of the method for evaluating hand function in stroke patients according to the present invention;

[0054] Figure 2 It is a schematic diagram of the action paradigm of the present invention;

[0055] Figure 3 It is a flow chart of the action paradigm execution of the present invention;

[0056] Figure 4 This is a schematic diagram of the EEG leads used in the present invention;

[0057] Figure 5 A schematic diagram of the myoelectric tissue used in the present invention;

[0058] Figure 6 The present invention is a flowchart of the steps of the method for evaluating hand function of stroke patients. DETAILED DESCRIPTION

[0059] The present invention proposes a method for evaluating hand function of stroke patients, aiming to propose a method for evaluating hand rehabilitation of stroke patients based on brain electromyography signals.

[0060] The hand function evaluation method for stroke patients proposed by the present invention will be described in the following specific embodiments:

[0061] In the technical solution of this embodiment, Figure 6 As shown, a method for evaluating hand function of a stroke patient comprises the following steps:

[0062] Step 1, signal acquisition and preprocessing: Combined with the Brunnstrom rating scale, the action paradigm was set, and the synchronous EEG signals and EMG signals of patients with different levels of stroke were collected at the same time, and the EEG signals and EMG signals were preprocessed respectively;

[0063] Step 2, decomposition of brain and electromyographic signals: Synchronously decompose the preprocessed brain and electromyographic signals based on wavelet packet transform, including constructing a wavelet packet function and selecting a wavelet packet basis function, decomposing the original signal, using a multi-resolution decomposition algorithm to calculate the wavelet coefficients of each level, and obtaining the sub-band signals of the original signal in each frequency according to the wavelet coefficients. The wavelet approximation coefficients and the wavelet coefficients are reconstructed using a multi-resolution reconstruction algorithm, and the reconstructed signals are separated according to the active bands of EEG and EMG, and the individual frequency domain signals are saved;

[0064] Step 3, extract phase information using Hilbert transform: convert the decomposed brain and electromyographic signals into time series, use discrete Hilbert transform to convert the time series into complex signals, calculate the instantaneous phase by calculating the phase angle of the complex signal, calculate the instantaneous amplitude, and perform phase verification;

[0065] Step 4: Use phase-amplitude coupling to estimate the modulation index: Cut the phase φ(n) into n slices, and the phase is distributed into n intervals, each interval is 360 / n°; take the average value of the amplitude a(t) in each demarcated interval, and use<a(t)> φ Indicates that the probability distribution P is obtained by dividing the amplitude in each interval by the sum of the intervals; the modulation index is obtained using the Kullback-Leibler distance;

[0066] Step 5, assessment of upper limb rehabilitation level: Based on the Spearman correlation coefficient, the fitting relationship between each patient's MI and the Brunnstrom rating scale was mapped, and the FCMC characteristic indicators and change trends that were significantly correlated with the Brunnstrom rating scale level were analyzed to quantitatively assess the disease level of stroke patients.

[0067] Furthermore, in step 1, the EEG signals of the stroke patients at leads Fp1, Fp2, F3, F4, C3, C4, Cz, P3, P4, A1, and A2 are collected respectively, and the EMG signals at the anterior bundle of the deltoid muscle, the middle bundle of the deltoid muscle, the biceps brachii, the triceps brachii, and the extensor digitorum of the forearm are collected.

[0068] The preprocessing operations of EEG signals include: re-referencing, baseline correction, filtering, removal of electrooculogram and EEG data segmentation; the preprocessing operations of EMG signals include: baseline correction, filtering and EMG data segmentation.

[0069] Furthermore, in step 2, the wavelet transform performs multi-resolution analysis on the signal through the scaling function φ(t) and the wavelet function ψ(t); the orthogonality condition is satisfied, and the wavelet basis function is generated by scaling and translating:

[0070] φ j,k (t) = 2 j / 2 φ(2 j tk),ψ j,k (t) = 2 j / 2 ψ(2 j tk);

[0071] Among them, j is the scale parameter and k is the translation parameter;

[0072] At the jth layer, the wavelet packet basis function Defined as:

[0073]

[0074] In the jth layer, the wavelet packet function of the nth node;

[0075] h k (t): low-pass filter coefficient;

[0076] g k (t): high-pass filter coefficient;

[0077] The original signal is gradually disassembled using a decomposition algorithm and compared with the wavelet function. Assuming the original function is F, the original function is decomposed into F1, F2, F3, F4... step by step; and with the wavelet part W1, W2, W3, W4..., F1=FJ-1+WJ-1, decomposing to the 0th level;

[0078] The signal of each layer passes through a low-pass filter and a high-pass filter to calculate the decomposition coefficients:

[0079]

[0080] The signal value of the nth node in the jth layer;

[0081] h[m], g[m]: low-pass and high-pass filter coefficients;

[0082] The wavelet coefficients at each level are calculated using a multiresolution decomposition algorithm;

[0083] According to the wavelet coefficients, the sub-band signals of the original signal in each frequency are obtained;

[0084] The wavelet approximation coefficients and wavelet coefficients are reconstructed using a multi-resolution reconstruction algorithm in the form of discrete filters; the signal is reconstructed using a linear combination of wavelet packet coefficients:

[0085]

[0086] Through the inverse filter h k and g k Realize layer-by-layer reconstruction;

[0087]

[0088] Separate the reconstructed signal according to the active bands of EEG and EMG; and decompose the EMG signal into the same frequency bands;

[0089] Save the individual frequency domain signals.

[0090] Furthermore, in step 3, the decomposed brain and myoelectric signals are converted into time series;

[0091] Use discrete Hilbert transform to transform the time series into a complex signal;

[0092]

[0093] The instantaneous phase is obtained by calculating the phase angle of the complex signal;

[0094] φ(t)=arg(z(t));

[0095] The instantaneous amplitude is calculated by the calculation formula;

[0096]

[0097] Compensate between phase differences;

[0098] φ unwrapped (n) = φ unwrapped (n-1)+Δφ(n);

[0099] in:

[0100] Δφ(n)=φ unwrapped (n)-φ unwrapped (n-1);

[0101] In order to ensure phase continuity, Δφ(n) needs to be adjusted;

[0102]

[0103] The final unwrapped phase is:

[0104] φ unwrapped (n) = φ unwrapped (n-1)+Δφ adjusted (n);

[0105] Perform a phase check.

[0106] Embodiment 1:

[0107] A method for evaluating upper limb rehabilitation of stroke patients, the specific process is as follows Figure 1 As shown, the following steps are included:

[0108] Step 1: Signal acquisition and preprocessing. Under the guidance of clinicians, arm flexion was set as the action paradigm in combination with the Brunnstrom rating scale, and the synchronous EEG signals and surface electromyographic signals of several stroke patients were collected when they performed arm flexion, ensuring that at least 5 subjects of each level of Brunnstrom were included in the stroke subjects.

[0109] The movement paradigm includes two movements: arm flexion and arm opening, such as Figure 2 As shown in the figure. When the subject is performing arm flexion, a prompter is placed in front of the subject to give the subject a picture and sound prompt. During the signal collection process, the subject needs to sit upright on the chair, with the arm hanging naturally, keep the body stable, and reduce unnecessary movements, including but not limited to: turning the head, swallowing, blinking, etc.; Figure 3 The figure shows the specific action execution process. The preparation stage is from 0 to 10 seconds, and the subjects are given voice prompts. In the action execution stage, the subjects need to slowly straighten their arms forward until their arms are parallel to the ground, and then slowly raise their forearms until they are parallel to the body and remain stable. The action execution stage lasts for 16 seconds. In the arm opening stage, the subjects need to slowly open their arms to both sides of the body until their arms are fully opened and parallel to the ground. The arm opening stage lasts for 16 seconds. Each subject needs to complete 5 test groups of actions. One test group of actions includes 5 arm flexion actions and 5 arm opening actions. After completing one test group of actions, the subject will be given a 10-minute rest period to prevent mental and muscle fatigue.

[0110] In the EEG acquisition process, the international 10-20 system is strictly followed. The subjects need to clean their scalp in advance and wear the EEG cap correctly. The reference point is the central lead Cz. To avoid distortion of the collected EEG signal, the impedance between the electrode and the scalp must be less than 5kΩ before the EEG signal is collected. Figure 4 As shown, EEG is collected from leads Fp1, Fp2, F3, F4, C3, C4, Cz, P3, P4, A1, and A2 respectively.

[0111] During the sEMG acquisition process, alcohol was used to clean the skin of the subject's upper arm, and scrub was used to polish the subject's local skin when necessary to reduce skin impedance. Figure 5 As shown, sEMGs were collected from the anterior deltoid, middle deltoid, biceps, triceps and extensor digitorum of the forearm.

[0112] The EEG preprocessing operation includes but is not limited to re-referencing, baseline correction, filtering, electrooculogram removal, and EEG data segmentation. In the re-referencing operation, A1 and A2 are selected as new reference points; in the baseline correction operation, the baseline drift of EEG is reduced by the de-averaging method; in the filtering operation, the EEG is subjected to 0.1-100Hz bandpass filtering and 50Hz notch processing; in the electrooculogram removal operation, the ICA algorithm is used to remove the subject's electrooculogram artifacts; in the EEG data segmentation operation, the time range of the action execution stage is determined based on the marks in the EEG acquisition system, and the corresponding data segments are extracted.

[0113] The preprocessing operation of the sEMG includes but is not limited to baseline correction, filtering, electromyographic data segmentation, etc. In the baseline correction operation, the baseline drift of the sEMG is reduced by taking the average method; in the filtering operation, the sEMG is subjected to 0.1-100Hz bandpass filtering and 50Hz notch processing; in the electromyographic data segmentation operation, the time range of the action execution stage is determined based on the marks in the electromyographic acquisition system, and the corresponding data segment is extracted.

[0114] Step 2: Decomposition of brain electromyographic signals. This embodiment uses wavelet transform to achieve synchronous decomposition of brain electromyographic signals, which specifically includes the following contents:

[0115] 2.1 Construct wavelet packet function and select wavelet packet basis function:

[0116] Wavelet transform performs multi-resolution analysis (MRA) on the signal through the scaling function φ(t) and the wavelet function ψ(t).

[0117] Scaling function: represents the low-frequency components of the signal.

[0118] Wavelet function: represents the high-frequency components of the signal.

[0119] They satisfy the orthogonality condition and generate wavelet basis functions by scaling and translating:

[0120] φ j,k (t) = 2 j / 2 φ(2 j tk),ψ j,k (t) = 2 j / 2 ψ(2 j tk);

[0121] Where j is the scale parameter and k is the translation parameter

[0122] At the jth layer, the wavelet packet basis function Defined as:

[0123]

[0124] In the jth layer, the wavelet packet function of the nth node;

[0125] h k (t): low-pass filter coefficient;

[0126] g k (t): high-pass filter coefficient;

[0127] 2.2 Decompose the original signal:

[0128] The original signal is gradually disassembled using a decomposition algorithm and compared with the wavelet function. Assuming the original function is F, decompose it step by step into F1, F2, F3, F4... and the wavelet parts W1, W2, W3, W4... That is, F1=FJ-1+WJ-1, and the decomposition continues in this way until it ends at level 0.

[0129] The signal of each layer passes through a low-pass filter h k and high pass filter g k Calculate the decomposition coefficients:

[0130]

[0131] The signal value of the nth node in the jth layer;

[0132] h[m], g[m]: low-pass and high-pass filter coefficients;

[0133] 2.3 Use the multi-resolution decomposition algorithm to calculate the wavelet coefficients at each level.

[0134] 2.4 Based on the solved wavelet coefficients, the sub-band signals of the original signal at each frequency are obtained.

[0135] The result of wavelet packet decomposition is a binary tree structure: each node represents the component of the signal in a specific frequency band; the depth of the tree determines the number of decomposition layers, and the deeper the layer, the higher the resolution of the signal frequency. Therefore, the initial setting value means that each wavelet packet layer represents a subband signal.

[0136] 2.5 Use the multi-resolution reconstruction algorithm to reconstruct the approximate coefficients and wavelet coefficients using discrete filter implementation. The signal is reconstructed using a linear combination of wavelet packet coefficients:

[0137]

[0138] Through the inverse filter h k and g k Realize layer-by-layer reconstruction;

[0139]

[0140] 2.6 Separate the reconstructed signals based on the active bands of EEG and EMG.

[0141] The frequency band of EEG signals is usually divided into several typical frequency bands according to different frequency ranges:

[0142] Delta 0.5Hz-4Hz;

[0143] Theta 4Hz-8Hz;

[0144] Alpha 8Hz-13Hz;

[0145] Beta 13Hz-30Hz;

[0146] Gamma 30Hz-100Hz;

[0147] They correspond to different activity states and functions of the brain. The reconstructed EEG signal is decomposed into five frequency bands, and the EMG signal is decomposed into the same frequency bands.

[0148] 2.7 Save the individual frequency domain signals.

[0149] Step 3: Use Hilbert transform to extract phase information, which includes the following:

[0150] 3.1 Convert the split signal into a time series x(t);

[0151] 3.2 Use Hilbert transform to transform the time series into a complex signal.

[0152]

[0153] The Hilbert transform is usually simplified by Fourier transform. The Hilbert transform in the frequency domain is represented by shifting the phase of the negative frequency components of the signal by -90 degrees and the phase of the positive frequency components by +90 degrees.

[0154]

[0155] The analytical signal z(t) is the product of the original real-valued signal x(t) and its Hilbert transform The complex valued signal

[0156] 3.3 The instantaneous phase is obtained by calculating the phase angle of the complex signal.

[0157]

[0158] 3.4 Calculate the instantaneous amplitude through the calculation formula.

[0159]

[0160] 3.5 Phase unwrapping to ensure phase continuity

[0161] φ unwrapped (n) = φ unwrapped (n-1)+Δφ(n);

[0162] in:

[0163] Δφ(n)=φ unwrapped (n)-φ unwrapped (n-1);

[0164] In order to ensure phase continuity, Δφ(n) needs to be adjusted:

[0165]

[0166] The final unwrapped phase is:

[0167] φ unwrapped (n) = φ unwrapped (n-1)+Δφ adjusted (n);

[0168] 3.5 Perform phase check. Compare the calculated phase with the original signal phase to determine the accuracy of the reconstructed signal phase.

[0169] Step 4: Use phase amplitude coupling (PAC) to estimate the modulation index (MI), which includes the following:

[0170] 4.1 In order to better measure the adaptability of PAC, the phase φ(n) is first cut into n slices. The phase is divided into n intervals, each interval is 360 / n°

[0171] 4.2 The average value of the amplitude a(t) is taken in each delimited interval and is expressed as φ >(t) means that the probability distribution P is obtained by dividing the amplitude in each interval by the sum of the intervals.

[0172]

[0173] in P(j) represents the normalized amplitude in an interval. This distribution can be used to use the modulation index.

[0174] 4.3 Modulation index (MI) is calculated by Kullback-Leibler distance, which is used to measure the deviation between the amplitude probability distribution P and the uniform distribution Q (i.e., the amplitude values ​​are uniformly distributed in the range [-1,1]):

[0175]

[0176] in:

[0177]

[0178] For a uniform distribution, Q(k)=1 / n, we can also get

[0179]

[0180] Step 5: In the upper limb rehabilitation grade assessment, the consistency relationship between the modulation parameters estimated by phase amplitude coupling and the clinical assessment scale is explored based on the Spearman method. The calculation process of the Spearman correlation coefficient is as follows:

[0181]

[0182] Where ρ is the Spearman correlation coefficient, h i is the rank difference of each team's observations. In the present invention, each team's observations are divided into MI and Brunnstrom ranks. num is the total number of observations. After calculating the two ρ values ​​respectively, the mean ρ is obtained. mean , to characterize the fitting relationship between each patient's MI and the Brunnstrom rating scale, analyze the FCMC characteristic indicators and change trends that are significantly correlated with the Brunnstrom rating scale level, and complete the quantitative assessment of the disease level of stroke patients.

[0183] ​The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating hand function in stroke patients, characterized in that: The following steps are involved: Step 1, signal acquisition and preprocessing: Combined with the Brunnstrom rating scale, the action paradigm was set, and the synchronous EEG signals and EMG signals of patients with different levels of stroke were collected at the same time, and the EEG signals and EMG signals were preprocessed respectively; Step 2, decomposition of brain and electromyographic signals: Synchronously decompose the preprocessed brain and electromyographic signals based on wavelet packet transform, including constructing a wavelet packet function and selecting a wavelet packet basis function, decomposing the signal, using a multi-resolution decomposition algorithm to calculate the wavelet coefficients of each level, and obtaining the sub-band signals of the original signal in each frequency according to the wavelet coefficients. The wavelet approximation coefficients and the wavelet coefficients are reconstructed using a multi-resolution reconstruction algorithm, and the reconstructed signals are separated according to the active bands of EEG and EMG, and the individual frequency domain signals are saved; Step 3, extract phase information using Hilbert transform: convert the decomposed brain and electromyographic signals into time series, use discrete Hilbert transform to convert the time series into complex signals, calculate the instantaneous phase by calculating the phase angle of the complex signal, calculate the instantaneous amplitude, and perform phase verification; Step 4: Use phase-amplitude coupling to estimate the modulation index: Cut the phase φ(n) into n slices, and the phase is distributed into n intervals, each interval is 360 / n°; take the average value of the amplitude a(t) in each demarcated interval, and use <a(t) φ Indicates that the probability distribution P is obtained by dividing the amplitude in each interval by the sum of the intervals; the modulation index is obtained using the Kullback-Leibler distance; Step 5, assessment of upper limb rehabilitation level: Based on the Spearman correlation coefficient, the fitting relationship between each patient's MI and the Brunnstrom rating scale was mapped, and the FCMC characteristic indicators and change trends that were significantly correlated with the Brunnstrom rating scale level were analyzed to quantitatively assess the disease level of stroke patients.

2. The method for evaluating hand function of stroke patients according to claim 1, characterized in that: In step 1, the EEG signals of stroke patients at leads Fp1, Fp2, F3, F4, C3, C4, Cz, P3, P4, A1, and A2 are collected respectively, and the EMG signals of the anterior bundle of the deltoid muscle, the middle bundle of the deltoid muscle, the biceps brachii, the triceps brachii, and the extensor digitorum of the forearm are collected. The preprocessing operations of EEG signals include: re-referencing, baseline correction, filtering, removal of electrooculogram and EEG data segmentation; the preprocessing operations of EMG signals include: baseline correction, filtering and EMG data segmentation.

3. The method for evaluating hand function of stroke patients according to claim 1, characterized in that: In step 2, the wavelet transform performs multi-resolution analysis on the signal through the scaling function φ(t) and the wavelet function ψ(t); the orthogonality condition is satisfied, and the wavelet basis function is generated by scaling and translating: f j,k (t)=2 j / 2 φ(2 j tk),ψ j,k (t)=2 j / 2 ψ(2 j tk); Among them, j is the scale parameter and k is the translation parameter; At the jth layer, the wavelet packet basis function Defined as: In the jth layer, the wavelet packet function of the nth node; h k (t): low-pass filter coefficient; g k (t): high-pass filter coefficient; The original signal is gradually disassembled using a decomposition algorithm and compared with the wavelet function. Assuming the original function is F, the original function is decomposed into F1, F2, F3, F4... step by step; and with the wavelet part W1, W2, W3, W4..., F1=FJ-1+WJ-1, decomposing to the 0th level; The signal of each layer passes through a low-pass filter h k and high pass filter g k Calculate the decomposition coefficients: The signal value of the nth node in the jth layer; h[m], g[m]: low-pass and high-pass filter coefficients; The wavelet coefficients at each level are calculated using a multiresolution decomposition algorithm; According to the wavelet coefficients, the sub-band signals of the original signal in each frequency are obtained; The wavelet approximation coefficients and wavelet coefficients are reconstructed using a multi-resolution reconstruction algorithm in the form of discrete filters; the signal is reconstructed using a linear combination of wavelet packet coefficients: Through the inverse filter h k and g k Realize layer-by-layer reconstruction; Separate the reconstructed signal according to the active bands of EEG and EMG; and decompose the EMG signal into the same frequency bands; Save the individual frequency domain signals.

4. The method for evaluating hand function of stroke patients according to claim 1, characterized in that: In step 3, the decomposed brain and myoelectric signals are converted into time series; Use discrete Hilbert transform to transform the time series into a complex signal; The instantaneous phase is obtained by calculating the phase angle of the complex signal; φ(t)=arg(z(t)); The instantaneous amplitude is calculated by the calculation formula; Compensate between phase differences; f unwrapped (n)=φ unwrapped (n-1)+Δφ(n); in: Δφ(n)=φ unwrapped (n)-φ unwrapped (n-1); In order to ensure phase continuity, Δφ(n) needs to be adjusted; The final unwrapped phase is: f unwrapped (n)=φ unwrapped (n-1)+Δφ adjusted (n); Perform a phase check.

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