Method and device for coupling analysis of synchronous continuous blood pressure signal and electroencephalogram signal, and fatigue state evaluation method

Abstract

The application provides a synchronous continuous blood pressure signal and electroencephalogram coupling analysis method, which comprises the following steps: a synchronous signal preprocessing step; preprocessing the synchronous continuous blood pressure signal and single-channel electroencephalogram to obtain a clean continuous blood pressure signal and a clean electroencephalogram; a continuous blood pressure signal feature detection and processing step; identifying the peak points of the blood pressure waveform of each cycle of the clean continuous blood pressure signal; each peak point corresponds to a systolic pressure anchor point; an evoked potential construction step; taking each systolic pressure anchor point as a reference, intercepting a clean electroencephalogram of a predetermined length from the clean electroencephalogram to form an intercepted segment; aligning and averaging all the intercepted segments to obtain a systolic pressure evoked potential; and a fatigue evaluation index calculation step; using a P300 paradigm to analyze the systolic pressure evoked potential to obtain a systolic pressure evoked potential P300 response as a fatigue evaluation index.

Description

Technical Field

[0001] This application belongs to the field of biomedical signal processing and human physiological state monitoring technology, and particularly relates to a method and device for coupling analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, as well as a method for assessing fatigue state. Background Technology

[0002] Fatigue, as a typical manifestation of declining human physiological function, is crucial for accurate assessment to ensure workplace safety and maintain health. Currently, mainstream fatigue assessment methods are mainly divided into two categories: subjective scale assessment and quantitative assessment based on single physiological signals, but both have significant limitations.

[0003] Common subjective scales include the Fatigue Severity Scale (FSS), the Minimum Mental State Examination (MMSE), and the Profile of Mood States (POMS). These methods assess fatigue through questionnaires or verbal reports, and have several drawbacks: First, they are highly subjective, with assessment results significantly influenced by the participant's emotions, cognitive biases, and social expectation effects. Second, they lack dynamism, as the scales provide snapshot-like measurements, failing to capture the continuous dynamic evolution of fatigue from mild accumulation to severe onset, thus hindering real-time early warning capabilities. Third, individual differences exist, with varying subjective understandings of fatigue among participants, resulting in a lack of a unified objective reference standard for the scale results.

[0004] Common single physiological signal assessments include electrocardiogram (ECG), electroencephalogram (EEG), and photo-plethysmography (PPG), which extract features to quantify fatigue. However, due to the limited physiological information coverage of a single signal, single physiological signal assessment has the following problems: EEG can directly reflect central nervous system activity (e.g., increased (θ + α) / β, decreased θ / α or β / α indicate worsening fatigue), but these indicators only characterize central fatigue and cannot correlate the relationship between the peripheral circulatory system and fatigue; ECG indirectly correlates fatigue through heart rate variability analysis, time-domain and frequency-domain feature extraction, and nonlinear feature extraction; PPG indirectly correlates fatigue through waveform features and pulse wave conduction time, but neither can directly reflect the inhibitory state of the central nervous system and is significantly affected by factors such as exercise and emotional fluctuations, resulting in insufficient accuracy when used alone. Heartbeat evoked potentials (HEPs) analyze EEG amplitude using the QRS wave of the ECG as an anchor point. There have been some studies on their use in attention assessment, but they have not been used for fatigue assessment.

[0005] The physiological mechanism of fatigue is the result of the synergistic effect of the central nervous system and the peripheral circulatory system—central inhibition leads to a decrease in peripheral blood pressure regulation, while peripheral blood pressure fluctuations, in turn, affect central blood supply and neural activity. Current technologies lack a method for synchronously analyzing the correlation between these two systems, resulting in a single assessment dimension that cannot fully cover the physiological mechanisms of fatigue. Therefore, there is an urgent need for a new method that can integrate physiological information from multiple systems and objectively and accurately quantify fatigue status. Summary of the Invention

[0006] In view of the above problems, this application aims to propose a method for the coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, a device for the coupled analysis of synchronous continuous blood pressure signals and EEG signals, and a method for assessing fatigue status.

[0007] The method for coupling analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals in this application includes:

[0008] Synchronous signal preprocessing steps: The synchronized continuous blood pressure signal and single-channel EEG signal are preprocessed to obtain a clean continuous blood pressure signal and a clean EEG signal.

[0009] Continuous blood pressure signal feature detection and processing steps: Identify the peak points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; Each peak point corresponds to a systolic blood pressure anchor point;

[0010] Steps for constructing evoked potentials: Using each systolic pressure anchor point as a reference, extract clean EEG signals of a predetermined duration from the clean EEG signals to form a segment; Align and average all the segments to obtain systolic pressure evoked potentials;

[0011] The steps for calculating fatigue evaluation indicators are as follows: The P300 paradigm is used to analyze the systolic pressure evoked potentials, and the P300 response of the systolic pressure evoked potentials is obtained as a fatigue evaluation indicator.

[0012] The method for coupling analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals in this application includes:

[0013] Synchronous signal preprocessing steps: The synchronized continuous blood pressure signal and single-channel EEG signal are preprocessed to obtain a clean continuous blood pressure signal and a clean EEG signal.

[0014] Continuous blood pressure signal feature detection and processing steps; identifying the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each trough point corresponds to a diastolic pressure anchor point;

[0015] Steps for constructing evoked potentials: Using each diastolic pressure anchor point as a reference, extract clean EEG signals of a predetermined duration from the clean EEG signals to form a segment; Align and average all the segments to obtain diastolic pressure evoked potentials;

[0016] Fatigue evaluation index calculation steps: Use the P300 paradigm to analyze diastolic evoked potentials and obtain the diastolic evoked potential P300 response as a fatigue evaluation index.

[0017] The method for coupling analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals in this application includes:

[0018] Synchronous signal preprocessing steps: The synchronized continuous blood pressure signal and single-channel EEG signal are preprocessed to obtain a clean continuous blood pressure signal and a clean EEG signal.

[0019] Continuous blood pressure signal feature detection and processing steps: Identify the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; Each trough point corresponds to a diastolic pressure anchor point; For any two adjacent diastolic pressure anchor points, if the diastolic pressure corresponding to the later diastolic pressure anchor point is greater than the diastolic pressure corresponding to the earlier diastolic pressure anchor point, then the later diastolic pressure anchor point is determined to be a vasopressor anchor point.

[0020] The steps for constructing evoked potentials are as follows: Using each escalating anchor point as a reference, a predetermined duration of clean EEG signal is extracted from the clean EEG signal to form a segment; All segments are aligned and averaged to obtain diastolic evoked potentials.

[0021] Fatigue evaluation index calculation steps: Use the P300 paradigm to analyze diastolic evoked potentials and obtain the diastolic evoked potential P300 response as a fatigue evaluation index.

[0022] The synchronous continuous blood pressure signal and electroencephalogram (EEG) signal coupling analysis device of this application includes:

[0023] The synchronous signal preprocessing unit is used to preprocess the synchronous continuous blood pressure signal and single-channel EEG signal to obtain a clean continuous blood pressure signal and a clean EEG signal.

[0024] The continuous blood pressure signal feature detection and processing unit is used to identify the peak points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each peak point corresponds to a systolic blood pressure anchor point.

[0025] The evoked potential construction unit uses each systolic pressure anchor point as a reference to extract clean EEG signals of a predetermined duration to form a segment; all the segments are aligned and averaged to obtain systolic pressure evoked potentials.

[0026] The fatigue evaluation index calculation unit uses the P300 paradigm to analyze the systolic pressure evoked potential and obtains the systolic pressure evoked potential P300 response as a fatigue evaluation index.

[0027] The synchronous continuous blood pressure signal and electroencephalogram (EEG) signal coupling analysis device of this application includes:

[0028] The synchronous signal preprocessing unit is used to preprocess the synchronous continuous blood pressure signal and single-channel EEG signal to obtain a clean continuous blood pressure signal and a clean EEG signal.

[0029] The continuous blood pressure signal feature detection and processing unit is used to identify the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each trough point corresponds to a diastolic pressure anchor point.

[0030] The evoked potential construction unit uses each diastolic pressure anchor point as a reference to extract clean EEG signals of a predetermined duration from clean EEG signals to form a segment; all the segments are aligned and averaged to obtain diastolic pressure evoked potentials.

[0031] The fatigue evaluation index calculation unit uses the P300 paradigm to analyze diastolic evoked potentials and obtains the diastolic evoked potential P300 response as a fatigue evaluation index.

[0032] The synchronous continuous blood pressure signal and electroencephalogram (EEG) signal coupling analysis device of this application includes:

[0033] The synchronous signal preprocessing unit is used to preprocess the synchronous continuous blood pressure signal and single-channel EEG signal to obtain a clean continuous blood pressure signal and a clean EEG signal.

[0034] The continuous blood pressure signal feature detection and processing unit is used to identify the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each trough point corresponds to a diastolic pressure anchor point; for any two adjacent diastolic pressure anchor points, if the diastolic pressure corresponding to the diastolic pressure anchor point in the later time position is greater than the diastolic pressure corresponding to the diastolic pressure anchor point in the earlier time position, then the diastolic pressure anchor point in the later time position is determined to be a vasopressor anchor point.

[0035] The evoked potential construction unit uses each boosting anchor point as a reference to extract clean EEG signals of a predetermined duration from the clean EEG signals to form a segment; all the segments are aligned and averaged to obtain diastolic evoked potentials.

[0036] The fatigue evaluation index calculation unit uses the P300 paradigm to analyze diastolic evoked potentials and obtains the diastolic evoked potential P300 response as a fatigue evaluation index.

[0037] The fatigue state assessment method of this application uses the fatigue evaluation index obtained by the aforementioned method to analyze the fatigue of the subject.

[0038] Preferably, when a subject's fatigue evaluation index is lower than a predetermined threshold, the subject is considered to be in a state of fatigue.

[0039] The synchronous continuous blood pressure signal and electroencephalogram signal coupling analysis method, device and fatigue state assessment method of this application overcome the subjectivity and inaccuracy of subjective scale assessment, break through the limitations of single physiological signal analysis, provide more comprehensive fatigue state information through multimodal signal fusion, and thus provide a stable physiological indicator that can directly and objectively quantify the blood pressure-brain coupling relationship to more accurately assess the degree of fatigue. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the proposed method for the coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals.

[0041] Figure 2 The original CBP and EEG signals in the example, along with their locally amplified signals in the 210-240s segment.

[0042] Figure 3 The examples show the preprocessed CBP and EEG signals and their locally amplified signals in the 210-240s segment.

[0043] Figure 4The results show the preprocessed CBP signal SBP and DBP position detection results in the example.

[0044] Figure 5 This is a schematic diagram illustrating the process of obtaining the indices SBP_P300_AM and DBP_P300_AM, which characterize the fatigue state, in the example.

[0045] Figure 6 This is a schematic diagram illustrating the process of obtaining the fatigue state index DBP_AS_P300_AM in the example.

[0046] Figure 7 Spearman correlation analysis of the fatigue state indicators SBP_P300_AM, DBP_P300_AM, and DBP_AS_P300_AM obtained in the example.

[0047] Figure 8 This is a comparison chart showing the discriminative power of the fatigue state indicators SBP_P300_AM, DBP_P300_AM, and DBP_AS_P300_AM obtained in the example. Detailed Implementation

[0048] The present application will now be described in detail with reference to the accompanying drawings.

[0049] The synchronous continuous blood pressure signal and electroencephalogram (EEG) signal coupling analysis method of this application is combined with the appendix. Figure 1 Please provide a detailed explanation.

[0050] 1. Synchronization signal preprocessing.

[0051] The continuously acquired blood pressure (CBP) and electroencephalogram (EEG) signals were processed as follows:

[0052] 1) CBP signal: Baseline drift is removed by a Butterworth high-pass filter with a cutoff frequency of 0.5Hz, motion artifacts are reduced by a Butterworth low-pass filter with a cutoff frequency of 5Hz, and finally power frequency interference is eliminated by a 50Hz notch filter to obtain a clean CBP signal.

[0053] 2) EEG Signal: For single-channel EEG signals, a 50Hz notch filter was used to eliminate power frequency interference. EEG data was filtered through a (0.05-100)Hz bandpass filter to remove non-physiological artifacts. Then, Daubechiesdb4 discrete wavelet transform was used to perform wavelet transform on the EEG to obtain approximate and detail components. These components were then processed using complete empirical mode decomposition with adaptive noise to calculate intrinsic mode functions (EMFs). Subsequently, independent component analysis (ICA) was used to calculate the independent components of the EMFs, followed by sample entropy quantization. Independent components whose sample entropy values ​​satisfy the Gomez-Herrero condition were considered artifacts and set to zero. Finally, inverse ICA was performed to reconstruct new approximate and detail components, resulting in a clean EEG signal free of cardiac electric field artifacts, eye movement artifacts, and electromyography (EMG) artifacts.

[0054] 2. CBP signal feature point detection and processing.

[0055] This system utilizes first-order differential analysis and an adaptive thresholding method to automatically identify the peak points (corresponding to systolic blood pressure SBP) and trough points (corresponding to diastolic blood pressure DBP) of the blood pressure waveform in each cycle. Specifically, the first derivative of the clean CBP signal is calculated to identify the point where the peak slope changes (the zero-crossing point where the slope changes from rising to falling). If no valid peak is detected for 2 consecutive seconds, the detection threshold is automatically lowered, and a lower limit (0.4 × average fluctuation range) is set to prevent oversensitivity. Based on all detected consecutive CBP signal peak points, a sequence of systolic blood pressure anchor point positions is obtained. and its corresponding anchor point contraction pressure sequence The diastolic blood pressure anchor point location sequence was obtained based on all detected consecutive CBP signal valleys. and its corresponding anchor point diastolic pressure sequence .

[0056] 3. Construct systolic blood pressure evoked potential (SBPEP) and diastolic blood pressure evoked potential (DBPEP).

[0057] Using each anchor point position determined in step 2 as a reference, extract an EEG segment centered on the anchor point, from 200ms before the anchor point to 600ms after the anchor point, from the synchronously acquired and preprocessed EEG signal. Align and average all extracted EEG segments. The EEG signal after anchor point averaging is defined as the SBPEP signal; [the signal will be...] The signal after averaging the anchor points is defined as the DBPEP signal.

[0058] 4. Extract the core fatigue evaluation index P300_AM.

[0059] Based on the SBPEP or DBPEP signals obtained in step 3, a time window of 250-350 ms after locating the anchor point is used to analyze the blood pressure-brain coupling effect using the classic P300 paradigm. The average amplitude of the EEG signal within this window is calculated, and this average amplitude value is defined as the systolic evoked potential P300 response SBP_P300_AM and the diastolic evoked potential P300 response DBP_P300_AM, respectively. This amplitude value is the core physiological indicator used to objectively evaluate fatigue status. The deeper the fatigue, the lower the amplitude of SBP_P300_AM and DBP_P300_AM, reflecting a reduction in the brain's allocation of cognitive processing resources to endogenous events such as blood pressure fluctuations.

[0060] 5. Optimization indicators based on DBP change trends.

[0061] During systole, strong pressure waves primarily propel blood forward, while during diastole, blood pressure (mainly DBP) is the primary driver maintaining continuous perfusion of the coronary arteries and cerebral microvessels. In a state of fatigue, cerebral hemodynamics change, and fluctuations in DBP directly affect the efficiency and stability of diastolic blood supply to the brain. Therefore, DBPEP, anchored to DBP, may more directly capture the neural activity regulation the brain employs to cope with changes in its baseline blood flow pressure. This regulation is weakened during fatigue, thus becoming more clearly reflected in DBPEP.

[0062] Furthermore, the brain's processing of bodily changes is asymmetrical. Typically, responses to challenging events consume more cognitive resources and generate more significant event-evoked potentials (such as P300). Elevated diastolic blood pressure can be seen as a positive, challenging condition, indicating that the body is in a state requiring preparation or alertness. The brain needs to allocate more neural resources to process such events. When fatigue depletes the overall cognitive resource pool, the performance decline on high-demand tasks is far greater than on low-demand tasks. Therefore, the attenuation of DBP evoked potentials at the bolus anchor point is more dramatic and significant during fatigue than that at the deboost anchor point, making it a more sensitive biomarker of fatigue.

[0063] Based on the above two points, using the diastolic blood pressure boosting anchor point as a reference is a preferred and efficient implementation of this invention. The specific implementation is as follows:

[0064] for , will the The DBP value of the shot and the first The DBP values ​​of the images were compared, among which... If the equation is satisfied Then define the first The DBP image shows the diastolic blood pressure boosting anchor point. The corresponding location of the diastolic blood pressure boosting anchor point is Using the diastolic pressure boosting anchor point as a reference, repeat steps 3-4 to calculate the corresponding average amplitude of P300, denoted as the diastolic pressure evoked potential P300 response DBP_AS_P300_AM, which can more accurately reflect the degree of fatigue.

[0065] Example

[0066] This method was validated using 298 healthy subjects with different fatigue states, such as... Figure 2-8 As shown. After completing the Fatigue Severity Scale (FSS) assessment, subjects simultaneously acquired CBP and EEG signals while lying down. FSS scores were then calculated, and traditional EEG-based fatigue state indices (θ + α) / β, θ / α, and β / α were calculated. The proposed method was then used to extract indices reflecting fatigue severity: SBP_P300_AM, DBP_P300_AM, and DBP_AS_P300_AM.

[0067] in, Figure 4 In the diagram, * represents systolic blood pressure and ○ represents diastolic blood pressure. Figure 5 In this study, the average values ​​of the SBPEP and DBPEP signals obtained from the EEG segments of the SBP and DBP anchor points and the averaged SBPEP and DBPEP signals are calculated for the SBPEP and DBPEP signals in the 250-350ms window after the anchor point. This yields the indices SBP_P300_AM and DBP_P300_AM that characterize the fatigue state. Figure 6 In this study, the fatigue state index DBP_AS_P300_AM can be obtained by averaging the EEG segments of the DBP boost anchor point and the evoked potential signals obtained after averaging the evoked potential signals in the 250-350ms window after the anchor point.

[0068] Based on the FSS scores of 298 subjects, the (θ+α) / β, θ / α, and β / α of EEG, as well as SBP_P300_AM, DBP_P300_AM, and DBP_AS_P300_AM, Spearman correlation analysis was performed. The results showed that the proposed indicators for quantifying fatigue severity, SBP_P300_AM (R=-0.123, P=0.034) and DBP_P300_AM (R=-0.1), are effective. The correlation and significance of the fatigue index DBP_AS_P300_AM (R=-0.122, P=0.036) with FSS were comparable to those of the traditional EEG-based indices θ / α (R=-0.122, P=0.036) and β / α (R=-0.137, P=0.018). However, the fatigue index DBP_AS_P300_AM, quantified based on the DBP boosting anchor point, showed the strongest correlation and significance with FSS (R=-0.158, P=0.006), outperforming fatigue indices extracted from traditional EEG analysis. Figure 7 As shown.

[0069] Based on the FSS scores of 298 subjects, subjects with FSS ≥ 27 (moderate to severe fatigue) were divided into one group and subjects with FSS < 27 (negligible fatigue that does not affect daily function) were divided into another group. ROC curves of the two groups were plotted using the SBP_P300_AM, DBP_P300_AM, and DBP_AS_P300_AM indices proposed in this invention to quantify the ability of the indices to distinguish the severity of fatigue, as shown in the figure below. The area under the curve is 0.5754 (standard error 0.0393). When the threshold is set to 0.88 μV, the sensitivity for differentiation is 81.13%. The area under the curve is 0.5947 (standard error 0.0403). When the threshold is set to 0.004 μV, the sensitivity for differentiation is 81.13%. The area under the curve is 0.6150 (standard error 0.0382). When the threshold is set to -0.022μV, the sensitivity for differentiation is 81.13%, such as... Figure 8 As shown.

[0070] Compared with the prior art, this application has the following significant advantages:

[0071] It boasts high objectivity and accuracy; based entirely on objective physiological signals, it eliminates subjective bias. By analyzing the physiological mechanism of blood pressure-brain coupling, it directly addresses the changes in the central nervous system's ability to respond to endogenous events in the cardiovascular system under fatigue, resulting in more accurate and reliable assessment results.

[0072] This method integrates multimodal information and exhibits strong anti-interference capabilities. It innovatively combines physiological information from two different sources: blood pressure (cardiovascular system) and electroencephalography (EEG) (central nervous system). This approach reduces sensitivity to specific artifacts of a single signal and further suppresses random noise through signal averaging techniques, resulting in more stable extracted physiological indicators.

[0073] The invention innovatively incorporates blood pressure evoked potentials (SBPEP / DBPEP), which is also the core innovation of this invention. Compared with traditional HEP, SBPEP / DBPEP is directly related to blood pressure, a physiological parameter crucial for brain perfusion and autonomic nervous activity, and can better reveal the neurovascular coupling mechanism related to fatigue, opening up a completely new analytical dimension for fatigue research.

[0074] The indicators are sensitive and allow for detailed analysis; by introducing an anchor point subdivision method based on blood pressure change trends (hysteresis), indicators across multiple dimensions can be obtained. This enables the invention not only to assess overall fatigue levels but also to detect more subtle changes in physiological states, resulting in higher sensitivity and specificity.

Claims

1. A method for coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, comprising: Synchronous signal preprocessing steps: The synchronized continuous blood pressure signal and single-channel EEG signal are preprocessed to obtain a clean continuous blood pressure signal and a clean EEG signal. Continuous blood pressure signal feature detection and processing steps: Identify the peak points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; Each peak point corresponds to a systolic blood pressure anchor point; Steps for constructing evoked potentials: Using each systolic pressure anchor point as a reference, extract clean EEG signals of a predetermined duration from the clean EEG signals to form a segment; Align and average all the segments to obtain systolic pressure evoked potentials; The calculation steps for fatigue evaluation indicators are as follows: After locating the systolic blood pressure anchor point, a time window of 250-350ms is established. The P300 paradigm is used to analyze the coupling effect between blood pressure and the brain. The average amplitude of the clean EEG signal within this time window is calculated. This average amplitude value is defined as the systolic blood pressure evoked potential P300 response SBP_P300_AM, which is used as the systolic blood pressure evoked potential P300 response as a fatigue evaluation indicator.

2. A method for coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, comprising: Synchronous signal preprocessing steps: The synchronized continuous blood pressure signal and single-channel EEG signal are preprocessed to obtain a clean continuous blood pressure signal and a clean EEG signal. Continuous blood pressure signal feature detection and processing steps; identifying the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each trough point corresponds to a diastolic pressure anchor point; Steps for constructing evoked potentials: Using each diastolic pressure anchor point as a reference, extract clean EEG signals of a predetermined duration from the clean EEG signals to form a segment; Align and average all the segments to obtain diastolic pressure evoked potentials; The steps for calculating fatigue evaluation indicators are as follows: After locating the diastolic pressure anchor point, a time window of 250-350ms is used. The P300 paradigm is used to analyze the coupling effect between blood pressure and the brain. The average amplitude of the clean EEG signal within this time window is calculated. This average amplitude value is defined as the diastolic pressure evoked potential P300 response DBP_P300_AM, which is used as the diastolic pressure evoked potential P300 response as a fatigue evaluation indicator.

3. A method for coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, comprising: Synchronous signal preprocessing steps: The synchronized continuous blood pressure signal and single-channel EEG signal are preprocessed to obtain a clean continuous blood pressure signal and a clean EEG signal. Continuous blood pressure signal feature detection and processing steps: Identify the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; Each trough point corresponds to a diastolic pressure anchor point; For any two adjacent diastolic pressure anchor points, if the diastolic pressure corresponding to the later diastolic pressure anchor point is greater than the diastolic pressure corresponding to the earlier diastolic pressure anchor point, then the later diastolic pressure anchor point is determined to be a vasopressor anchor point. The steps for constructing evoked potentials are as follows: Using each escalating anchor point as a reference, a predetermined duration of clean EEG signal is extracted from the clean EEG signal to form a segment; All segments are aligned and averaged to obtain diastolic evoked potentials. The steps for calculating fatigue evaluation indicators are as follows: After locating the systolic pressure anchor point, a time window of 250-350ms is used. The P300 paradigm is used to analyze the coupling effect between blood pressure and the brain. The average amplitude of the clean EEG signal within this time window is calculated. This average amplitude value is defined as the diastolic evoked potential P300 response DBP_AS_P300_AM, which is used as the diastolic evoked potential P300 response as a fatigue evaluation indicator.

4. The method for coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals according to any one of claims 1-3, characterized in that: In the synchronization signal preprocessing step For continuous blood pressure signals, baseline drift is removed by a Butterworth high-pass filter with a cutoff frequency of 0.5 Hz, motion artifacts are reduced by a Butterworth low-pass filter with a cutoff frequency of 5 Hz, and power frequency interference is eliminated by a 50 Hz notch filter to obtain a clean continuous blood pressure signal. For single-channel EEG signals, a 50Hz notch filter is used to eliminate power frequency interference; a 0.05-100Hz bandpass filter is used to remove non-physiological artifacts; wavelet transform is performed using Daubechies db4 discrete wavelets to obtain approximate and detail components, and then the components are processed by complete empirical mode decomposition with adaptive noise to calculate intrinsic mode functions; independent component analysis is used to calculate the independent components of the intrinsic mode functions, followed by sample entropy quantization; independent components whose sample entropy values ​​satisfy the Gomez-Herrero condition are considered artifacts and set to zero; inverse independent component analysis is performed to reconstruct new approximate and detail components, resulting in a clean EEG signal.

5. The method for coupled analysis of synchronous continuous blood pressure signals and electroencephalogram (EEG) signals according to any one of claims 1-3, characterized in that: In the continuous blood pressure signal feature detection and processing steps, the peak or trough value of the blood pressure waveform in each cycle is identified based on first-order differential analysis and adaptive thresholding method.

6. A device for coupling and analyzing synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, comprising: The synchronous signal preprocessing unit is used to preprocess the synchronous continuous blood pressure signal and single-channel EEG signal to obtain a clean continuous blood pressure signal and a clean EEG signal. The continuous blood pressure signal feature detection and processing unit is used to identify the peak points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each peak point corresponds to a systolic blood pressure anchor point. The evoked potential construction unit uses each systolic pressure anchor point as a reference to extract clean EEG signals of a predetermined duration to form a segment; all the segments are aligned and averaged to obtain systolic pressure evoked potentials. The fatigue evaluation index calculation unit uses a time window of 250-350ms after locating the systolic blood pressure anchor point. It uses the P300 paradigm to analyze the coupling effect of blood pressure and brain, calculates the average amplitude of the clean EEG signal within this time window, and defines the average amplitude value as the systolic blood pressure evoked potential P300 response SBP_P300_AM, which serves as the systolic blood pressure evoked potential P300 response as a fatigue evaluation index.

7. A device for coupling and analyzing synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, comprising: The synchronous signal preprocessing unit is used to preprocess the synchronous continuous blood pressure signal and single-channel EEG signal to obtain a clean continuous blood pressure signal and a clean EEG signal. The continuous blood pressure signal feature detection and processing unit is used to identify the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each trough point corresponds to a diastolic pressure anchor point. The evoked potential construction unit uses each diastolic pressure anchor point as a reference to extract clean EEG signals of a predetermined duration from clean EEG signals to form a segment; all the segments are aligned and averaged to obtain diastolic pressure evoked potentials. The fatigue evaluation index calculation unit uses a time window of 250-350ms after locating the diastolic pressure anchor point. It uses the P300 paradigm to analyze the coupling effect of blood pressure and brain, calculates the average amplitude of the clean EEG signal within this time window, and defines the average amplitude value as the diastolic pressure evoked potential P300 response DBP_P300_AM, which serves as the diastolic pressure evoked potential P300 response as a fatigue evaluation index.

8. A device for coupling and analyzing synchronous continuous blood pressure signals and electroencephalogram (EEG) signals, comprising: The synchronous signal preprocessing unit is used to preprocess the synchronous continuous blood pressure signal and single-channel EEG signal to obtain a clean continuous blood pressure signal and a clean EEG signal. The continuous blood pressure signal feature detection and processing unit is used to identify the trough points of the blood pressure waveform in each cycle of a clean continuous blood pressure signal; each trough point corresponds to a diastolic pressure anchor point; for any two adjacent diastolic pressure anchor points, if the diastolic pressure corresponding to the diastolic pressure anchor point in the later time position is greater than the diastolic pressure corresponding to the diastolic pressure anchor point in the earlier time position, then the diastolic pressure anchor point in the later time position is determined to be a vasopressor anchor point. The evoked potential construction unit uses each boosting anchor point as a reference to extract clean EEG signals of a predetermined duration from the clean EEG signals to form a segment; all the segments are aligned and averaged to obtain diastolic evoked potentials. The fatigue evaluation index calculation unit uses a time window of 250-350ms after locating the blood pressure escalation anchor point to analyze the blood pressure-brain coupling effect using the P300 paradigm. It calculates the average amplitude of the clean EEG signal within this time window and defines the average amplitude value as the diastolic blood pressure evoked potential P300 response DBP_AS_P300_AM, which serves as the diastolic blood pressure evoked potential P300 response for fatigue evaluation.

9. A fatigue state assessment method, which uses the fatigue evaluation index obtained by any one of claims 1-5 to analyze the fatigue of the subject, and considers the subject to be in a fatigue state when the subject's fatigue evaluation index is lower than a predetermined threshold.

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