Method and device for dynamically and quantitatively evaluating plateau stress state through cooperation of breathing and heart blood flow

Abstract

The invention provides a respiration-heart-blood flow synergistic effect analysis method based on synchronous continuous ECG, CBP and Breath signals, through continuous dynamic quantification of respiration-heart, respiration-blood flow coupling and heart-blood flow bidirectional coupling strength, a respiration-heart-blood flow synergistic index (RCH-SI) is comprehensively obtained, and the RCH-SI is used for plateau stress state evaluation. According to the method, the limitation of a traditional evaluation method based on single-mode physiological signals and even measurement discrete physiological features can be effectively solved, the defect that an existing method cannot quantify the synergistic effect of the respiration-heart-blood flow three systems is overcome, the accuracy of plateau stress state evaluation is improved, and the continuous, dynamic and real-time monitoring requirements of the plateau stress state are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of plateau health monitoring, and specifically relates to a frequency-band-specific coupling analysis method based on synchronized breath, electrocardiogram (ECG) and continuous blood pressure (CBP, reflecting blood flow status) signals. The method is used for the dynamic quantification of respiratory-cardiac-blood flow co-compensatory function in a low-pressure and low-oxygen environment at high altitudes (greater than 3000 meters). The method is suitable for early warning of acute mountain sickness (AMS) under plateau stress, plateau acclimatization assessment, and health management of plateau workers. Background Art

[0002] With the implementation of the strategy of building a modern China in all respects and the rapid development of transportation such as railways and aviation, more and more people from the plains are rapidly traveling to the plateau to participate in economic development, natural resource development, commercial trade, competitive sports, and tourism. However, without sufficient time to acclimate to the hypoxic environment of the plateau, rapid travel to altitudes above 3,000 meters can lead to altitude stress, manifested by a decrease in arterial oxygen partial pressure, which triggers activation of carotid body chemoreceptors, leading to a surge in ventilation, increased heart rate, and blood pressure fluctuations. If compensatory responses fail, the condition can progress to AMS, the most common form of high-altitude stress (with an incidence of 40%-90%), and can even worsen to high-altitude pulmonary edema (HAPE) or high-altitude cerebral edema (HACE). Therefore, accurate assessment of altitude stress is crucial for early warning and prevention of AMS susceptibility.

[0003] Given the rapid, convenient, and noninvasive nature of human physiological signals, blood oxygen saturation (SpO2), ECG, breath, blood pressure (BP), EEG, and body temperature are frequently used for monitoring, prediction, and early warning of AMS susceptibility. There are no significant differences between domestic and international approaches to target physiological signals, monitoring methods, and analysis techniques. Most studies primarily focus on discrete SpO2 measurements measured on-site, with a limited number of studies also examining heart rate, breath, blood pressure, EEG, and electrical impedance. Analysis of heart rate variability also primarily focuses on traditional time-domain (e.g., SDNN, pNN50) and frequency-domain (e.g., LF, HF, and LF / HF) feature parameters, with limited consideration of nonlinear indices and joint parameters. Furthermore, methods and metrics for integrating continuous multimodal physiological signals for continuous dynamic assessment are lacking. In summary, existing physiological signal-based research on AMS under altitude stress often focuses solely on a single physiological signal, neglecting the holistic nature of the human body and the interactions among multiple organs and systems. Furthermore, these studies often rely on sporadic measurements to obtain discrete physiological signal features, lacking continuous monitoring data support and continuous dynamic assessment methods and metrics for altitude stress status. Summary of the Invention

[0004] The present invention provides a respiratory-cardio-hemodynamic synergy analysis method based on continuous ECG, BP, and breath signals. By continuously and dynamically quantifying the respiratory-cardio, respiratory-hemodynamic coupling, and cardiac-hemodynamic bidirectional coupling strength, a comprehensive respiratory-cardio-hemodynamic synergy index (RCH-SI) is obtained for the assessment of high altitude stress status.

[0005] The method of the present application for dynamic quantitative evaluation of plateau stress state by coordinated respiratory and cardiac blood flow includes: signal preprocessing step, signal processing step, and respiratory-cardiac-blood flow synergy index sequence calculation step; In the signal preprocessing step, the synchronized Breath, ECG, and CBP signals are preprocessed to obtain preprocessed Breath, ECG, and CBP signals; In the signal processing step, the pre-processed ECG signal is detected, the QRS complex wave peak position is located, and the heart beat interval time series RR(n) is calculated; the pre-processed CBP signal is detected for the systolic pressure peak point of each cycle of the blood pressure waveform, and the systolic pressure time series is obtained based on all the detected continuous peak points. ; Preprocessed Breath, heart beat interval time series RR(n), systolic blood pressure time series , with frequency Resample to obtain the respiratory time series , cardiac time series , and blood flow time series ; In the respiratory-heart-blood flow synergy index sequence calculation step, a sliding window of predetermined length is used to traverse the respiratory time series with a predetermined sliding step size. , cardiac time series , and blood flow time series The total length of the respiratory time series within each sliding window during the sliding process , cardiac time series , and blood flow time series , calculate the coupling strength from respiration to heart within the sliding window , coupling strength from respiration to blood flow , the coupling strength from heart to blood flow , and the coupling strength of blood flow to the heart , use formula 5 to calculate the respiratory-cardiac-blood flow coordination index RCH-SI corresponding to the sliding window; (Formula 5); Among them, the weight coefficients α, β, and γ are determined by the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and cardiac-blood flow bidirectional feedback regulation, respectively; The respiratory-cardiac-blood flow coordination index RCH-SI calculated in each sliding window together constitutes a respiratory-cardiac-blood flow coordination index sequence reflecting the dynamic changes of the plateau stress state. .

[0006] Preferably, the dynamic changes of respiratory-cardiac-blood flow synergy index sequence By performing mean and median statistics and fine composite multi-scale entropy complexity calculations respectively, we can obtain the overall index characterizing the plateau stress state.

[0007] Preferably, the coupling strength from respiration to heart within the corresponding sliding window , coupling strength from respiration to blood flow , coupling strength from heart to blood flow , and the coupling strength of blood flow to the heart , calculated according to formula 1, formula 2, formula 3, and formula 4; (Formula 1); Where p is the model order, is the state vector, is a 3×3 coefficient matrix, is a white noise vector; Perform discrete Fourier transform and calculate the frequency domain transfer function matrix; (Formula 2); in, is the frequency domain transfer function matrix, for Elements in , represents the frequency domain transfer function of physiological signal j to physiological signal i. Specifically, for , the first column is breathing, the second column is heartbeat reflecting heart function, and the third column is blood pressure, then It represents the frequency domain transfer function of respiration to the heart, reflecting the frequency-specific effect of respiration on the heart; It represents the frequency domain transfer function of the heart to blood pressure, reflecting the frequency-specific effect of the heart on blood pressure; is a 3×3 identity matrix; (Formula 3); It represents the directional influence of physiological signal j on physiological signal i at a specific frequency f, with a value range of 0-1; correspond Elements in , physiological signal j and physiological signal i represent respiratory, cardiac, or blood flow signals, and j is not equal to i; (Formula 4); in, is the lower limit frequency of physiological signal j, the lower limit frequency of respiratory signal is 0.1Hz, and the lower limit frequency of heart and blood flow signal is 0.003Hz; is the upper limit frequency of physiological signal j, the upper limit frequency of respiratory signal is 0.3Hz, and the upper limit frequency of heart and blood flow signal is 0.4Hz; is the directional coupling strength from physiological signal j to physiological signal i; When j is the respiratory signal and i is the cardiac signal, the coupling strength from respiration to heart is obtained. , j is the respiratory signal and i is the blood flow signal, and the coupling strength from respiration to blood flow is obtained , j is the heart signal and i is the blood flow signal, and the coupling strength from the heart to the blood flow is obtained , j is the blood flow signal i is the heart signal and the coupling strength of blood flow to the heart .

[0008] Preferably, in the signal preprocessing step, for the Breath signal, a Butterworth bandpass filter with a cutoff frequency of 0.05-1.0 Hz is used to remove low-frequency motion artifacts and high-frequency ECG interference, and then a 50 Hz notch filter is used to suppress power frequency interference to obtain a preprocessed Breath signal; for the ECG signal, baseline drift is removed by median filtering, power frequency interference is suppressed by a 50 Hz notch filter, and high-frequency noise is eliminated by a 0.5-100 Hz Butterworth bandpass filter to obtain a preprocessed ECG signal; for the CBP signal, 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 preprocessed CBP signal.

[0009] Preferably, in the signal processing step, when detecting the preprocessed ECG signal, the QRS complex wave detection algorithm QRS_Detection is used to detect the preprocessed ECG signal; when detecting the peak point of the systolic pressure of each cycle of the blood pressure waveform, the first-order derivative of the preprocessed CBP signal is calculated to identify the peak slope change point. When no valid peak is detected for 2 consecutive seconds, the detection threshold is automatically lowered, and the lower limit of the threshold is set to 0.4 times the average fluctuation range.

[0010] The device for dynamic quantitative assessment of plateau stress state by coordinated respiratory and cardiac blood flow of the present application comprises: Signal preprocessing unit, signal processing unit, respiratory-cardiac-blood flow coordinated index sequence calculation unit; The signal preprocessing unit preprocesses the synchronous Breath, ECG, and CBP signals to obtain preprocessed Breath, ECG, and CBP signals; The signal processing unit detects the pre-processed ECG signal, locates the peak position of the QRS complex wave, and calculates the heart beat interval time series RR(n); for the pre-processed CBP signal, the peak point of the systolic pressure of each cycle of the blood pressure waveform is detected, and the systolic pressure time series is obtained based on all the detected continuous peak points. ; Preprocessed Breath, heart beat interval time series RR(n), systolic blood pressure time series , with frequency Resample to obtain the respiratory time series , cardiac time series , and blood flow time series ; The respiratory-cardiac-blood flow synergy index sequence calculation unit uses a sliding window of a predetermined length to traverse the respiratory time series with a predetermined sliding step size. , cardiac time series , and blood flow time series The total length of the respiratory time series within each sliding window during the sliding process , cardiac time series , and blood flow time series , calculate the coupling strength from respiration to heart within the sliding window , coupling strength from respiration to blood flow , the coupling strength from heart to blood flow , and the coupling strength of blood flow to the heart , use formula 5 to calculate the respiratory-cardiac-blood flow coordination index RCH-SI corresponding to the sliding window; (Formula 5); Among them, the weight coefficients α, β, and γ are determined by the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and cardiac-blood flow bidirectional feedback regulation, respectively; The respiratory-cardiac-blood flow coordination index RCH-SI calculated in each sliding window together constitutes a respiratory-cardiac-blood flow coordination index sequence reflecting the dynamic changes of the plateau stress state. .

[0011] The method of the present application can effectively solve the limitations of traditional evaluation methods based on single-modal physiological signals and occasional discrete physiological characteristics, overcome the defect that existing methods cannot quantify the synergistic effects of the respiratory-cardiac-blood flow systems, improve the accuracy of plateau stress state assessment, and meet the needs of continuous, dynamic, and real-time monitoring of plateau stress state. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Flow chart of the method for evaluating high altitude stress state by dynamic quantification of respiratory and cardiac blood flow coordination in this application.

[0013] Figure 2 Schematic diagram of the original Breath, ECG and CBP signals.

[0014] Figure 3 Schematic diagram of Breath, ECG and CBP signals after preprocessing and their local amplification.

[0015] Figure 4 Schematic diagram of the CBP feature point wave detection results and local amplification effect of the ECG signal after preprocessing.

[0016] Figure 5 To resample to the same fs Schematic diagram of the B(t), R(t) and S(t) sequences after PCR.

[0017] Figure 6 Schematic diagram of the respiratory-cardiac-blood flow coordination index RCH-SI calculated with a window width of 15s and a step sliding of 1s.

[0018] Figure 7 Schematic diagram of the dynamic sequence of respiratory-cardiac-blood flow coordination index RCH-SI(t) before, 24 hours after, and 72 hours after acute plateau entry.

[0019] Figure 8 Schematic diagram of the mean, median and complexity of the dynamic sequence of respiratory-cardiac-blood flow coordination index RCH-SI(t) before, 24 hours after and 72 hours after acute plateau entry. DETAILED DESCRIPTION

[0020] Below, this application is described in detail with reference to the accompanying drawings.

[0021] 1. Synchronous Breath, ECG, and CBP signal preprocessing 1) Breath signal: A Butterworth bandpass filter with a cutoff frequency of (0.05-1.0) Hz was used to remove low-frequency motion artifacts and high-frequency ECG interference. A 50 Hz notch filter was then used to suppress power frequency interference to obtain a clean breath signal.

[0022] 2) ECG signal: A clean ECG signal is obtained by removing baseline drift through median filtering, suppressing power frequency interference through a 50 Hz notch filter, and eliminating high-frequency noise through a Butterworth bandpass filter (0.5-100) Hz.

[0023] 3) CBP signal: Baseline drift is removed by using a Butterworth high-pass filter with a cutoff frequency of 0.5 Hz, motion artifacts are reduced by using a Butterworth low-pass filter with a cutoff frequency of 5 Hz, and finally power frequency interference is eliminated by using a 50 Hz notch filter to obtain a clean continuous blood pressure waveform.

[0024] 2. Feature point detection and processing 1) ECG signal QRS peak detection: The QRS complex wave detection algorithm QRS_Detection in the open source ECG signal processing toolbox ECGdeli is used to detect the pre-processed ECG signal, locate the QRS complex wave peak position, and then calculate the heart beat interval time series. .

[0025] 2) CBP signal peak detection: Based on first-order differential analysis and an adaptive threshold method, the peak SBP (corresponding to systolic pressure) of each blood pressure waveform cycle is automatically identified. Specifically, the first-order derivative of the clean CBP signal is calculated to identify the peak slope change point (the zero-crossing point where the slope changes from positive to negative, i.e., the slope changes from rising to falling). If no valid peak is detected for two consecutive seconds, the detection threshold is automatically lowered and a lower threshold limit (0.4× the average fluctuation range) is set to prevent oversensitivity. The systolic pressure time series is obtained based on all detected consecutive CBP peak points. .

[0026] 3. Resampling The preprocessed Breath signal and the heart beat interval time series obtained by detection and calculation , systolic blood pressure time series Resample to, and the resampling frequency is , representing breathing, heart, and blood flow respectively 、 、 Time series.

[0027] 4. Dynamic window sliding: Set the sliding window length to T and the sliding step to 1s (adjustable), and use this window to traverse 、 、 Total length, for each window in the sliding process 、 、 The time series is calculated in step 5. 5. Quantifying respiratory-cardiac-blood flow coordination indicators 1) Constructing a respiratory-cardiac-blood flow model: 、 、 The three time series are fitted with a multivariate autoregressive model according to Formula 1, and the coefficient matrix is ​​obtained , k=1,...,p (p is the model order).

[0028] (Formula 1); in, is the state vector, is a 3×3 coefficient matrix, is a white noise vector.

[0029] 2) Calculate the transfer function matrix in the frequency domain :By Fourier transform, the data based on breathing, heart and blood flow are transformed into 、 、 The model constructed by time series is converted to the frequency domain to obtain the transfer function matrix, as shown in Equation 2: (Formula 2); in, is the frequency domain transfer function matrix, for Elements in , represents the frequency domain transfer function of physiological signal j to physiological signal i. Specifically, for , the first column is breathing, the second column is heartbeat reflecting heart function, and the third column is blood pressure, then It represents the frequency domain transfer function of respiration to the heart, reflecting the frequency-specific effect of respiration on the heart; It represents the frequency domain transfer function of the heart to blood pressure, reflecting the frequency-specific influence of the heart on blood pressure. is a 3×3 identity matrix; 3) Calculation of directional influence: The partial directional coherence method is used to quantify the directional coupling strength from one physiological signal to another, as shown in Equation 3: (Formula 3); in, The directional influence of physiological signal j on physiological signal i at a specific frequency f, with a value range of (0-1); correspond Elements in , physiological signal j and physiological signal i represent respiratory, cardiac, or blood flow signals, and j is not equal to i;.

[0030] 4) Coupling strength calculation: In a specific frequency band with physiological significance, The total coupling strength in each direction is obtained by integration, as shown in Equation 4: (Formula 4); in, is the lower limit frequency of physiological signal j, the lower limit frequency of respiratory signal is 0.1Hz, and the lower limit frequency of heart and blood flow signal is 0.003Hz; is the upper limit frequency of physiological signal j, the upper limit frequency of respiratory signal is 0.3Hz, and the upper limit frequency of heart and blood flow signal is 0.4Hz; is the directional coupling strength from physiological signal j to physiological signal i. Existing studies have shown that breathing has a driving effect, that is, breathing affects heart rhythm (respiratory sinus arrhythmia) and vascular tension (blood pressure changes) through mechanical-neural mechanisms (such as changes in chest pressure, vagus nerve activity); and there is also a feedback mechanism between the heart and blood flow, that is, the pressure reflex affects blood pressure through changes in heart rate, and conversely, changes in blood pressure can also regulate heart rate, which manifests as a dynamic balance of the autonomic nervous system. Therefore, the present invention focuses on the unidirectional coupling from respiration to heart, the unidirectional coupling from respiration to blood flow, and the bidirectional coupling of cardiac blood flow. Specifically, j is the coupling strength from respiration to heart when i is a heart signal. , j is the respiratory signal and i is the blood flow signal, and the coupling strength from respiration to blood flow is obtained , j is the heart signal and i is the blood flow signal, and the coupling strength from the heart to the blood flow is obtained , j is the blood flow signal i is the heart signal and the coupling strength of blood flow to the heart .

[0031] 5) Define the respiratory-cardiac-blood flow synergy index: The multi-directional total coupling strength obtained in step 4) is integrated and the respiratory-cardiac-blood flow synergy index RCH-SI is calculated according to Equation 5.

[0032] (Formula 5); The weighting coefficients α, β, and γ are determined by the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and bidirectional feedback regulation of heart-blood flow, respectively. Specifically, the weight α for respiration to the heart is the highest (0.4-0.5), due to the strong physiological association with respiratory sinus arrhythmia. Vagal nerve endings are directly distributed in the sinoatrial node, and respiratory movement, through the pulmonary stretch receptor-vagal reflex, achieves millisecond-level heart rate regulation. Experimental data show that RSA contributes over 60% of heart rate variability at rest. Secondly, the weight β for respiration to blood flow is relatively low (0.2-0.3) because respiration indirectly affects blood flow, primarily through two pathways: fluctuations in venous return caused by changes in intrathoracic pressure (mechanical effect), and modulation of vascular sympathetic tone by the respiratory rhythm (neural effect). The latter has a delay of approximately 0.5 seconds and is weaker than RSA. The heart-blood flow bidirectional coupling weight γ (0.3-0.4) reflects the closed-loop baroreflex. The baroreflex exhibits bidirectional characteristics, with baroreceptors sensing changes in arterial blood pressure and triggering physiological mechanisms of cardiovascular regulation through neural conduction. When blood pressure rises, the incoming signals from the baroreceptors increase, inhibiting sympathetic nerve activity and enhancing parasympathetic nerve tone through neural circuits, reducing heart rate and peripheral resistance, and promoting a decrease in blood pressure. Conversely, when blood pressure decreases, the opposite mechanism causes blood pressure to rise again.

[0033] The linear model was chosen mainly based on the hierarchical integration of the driving effects of respiration on the heart and blood flow systems and the internal closed-loop feedback of the cardiovascular system, which conforms to the physiological cascade characteristics of "respiratory dominance → cardiovascular execution". The weight distribution scheme is essentially a mathematical characterization of the hierarchical regulation of the complete physiological synergistic system composed of the respiratory-vagal rapid pathway, respiratory-sympathetic vascular coupling, and pressure reflex closed-loop stability.

[0034] Calculating RCH-SI in each sliding window can form a respiratory-cardiac-blood flow synergy index sequence that reflects the dynamic changes of plateau stress state. , can be further The mean ( ), median ( ) statistics and fine composite multi-scale entropy ( ) complexity calculation; the mean includes: ( ), median values ​​include: , statistical and refined composite multiscale entropy values ​​include: 、 、 、 、 、 , and obtain the overall index characterizing the plateau stress state.

[0035] Examples Breath, ECG, and CBP signals were collected before, 24 hours after, and 72 hours after the subjects' rapid entry into the plateau (3,400 meters above sea level). The respiratory-cardiac-blood flow coordination index RCH-SI was extracted using this method at different time periods of the subjects' rapid entry into the plateau. The results showed that the subjects' RCH-SI was significantly higher 24 hours after the rapid entry into the plateau than before, and basically returned to the level before the rapid entry into the plateau 72 hours after the rapid entry into the plateau. This shows that the coordination of the respiratory, cardiac, and blood flow systems in the short term of rapid entry into the plateau is manifested as compensatory enhancement, that is, the three work together through the neuro-humoral regulatory mechanism to jointly cope with the challenges of the low-pressure and low-oxygen environment to the body. This synergistic enhancement is the core mechanism for the body to acutely adapt to low pressure and low oxygen. Figure 8 As shown, due to the physiological compensation caused by plateau stress, the average respiratory-cardiac-blood flow coordination index of the subjects 24 hours after acutely entering the plateau , median were significantly increased and recovered after 72 hours, while the complexity The levels of HgCl2 in the subjects were higher than those before the sudden plateau entry at 24 and 72 hours, indicating that the subjects entered a steady state under the stress of the plateau, and the respiratory-cardiac-blood flow synergy was constantly changing dynamically.

[0036] The method proposed in this paper is based on synchronized ECG, CBP, and Breath signals to perform frequency-band specific coupling analysis of respiratory-cardiac-blood flow synergy and to evaluate plateau stress state. The method realizes multi-dimensional coupling analysis through the synchronous quantification of three paths of respiratory-to-heart, respiratory-to-blood flow, and heart-blood flow bidirectional regulation. The PDC analysis technology is used to make the obtained coupling direction clearer, and the respiratory-cardiac-blood flow synergy index sequence is more accurate. It has dynamic characteristics and can meet the needs of continuous dynamic real-time monitoring of plateau stress status. It is suitable for AMS early warning under plateau stress, plateau acclimatization assessment and health management of plateau workers.

Claims

1. A method for dynamic quantitative assessment of plateau stress state by coordinated respiratory and cardiac blood flow, comprising: signal preprocessing step, signal processing step, and respiratory-cardiac-blood flow synergy index sequence calculation step; In the signal preprocessing step, the synchronized Breath, ECG, and CBP signals are preprocessed to obtain preprocessed Breath, ECG, and CBP signals; In the signal processing step, the pre-processed ECG signal is detected, the QRS complex wave peak position is located, and the heart beat interval time series RR(n) is calculated; the pre-processed CBP signal is detected for the systolic pressure peak point of each cycle of the blood pressure waveform, and the systolic pressure time series is obtained based on all the detected continuous peak points. ; Preprocessed Breath, heart beat interval time series RR(n), systolic blood pressure time series , with frequency Resample to obtain the respiratory time series , cardiac time series , and blood flow time series ; In the respiratory-heart-blood flow synergy index sequence calculation step, a sliding window of predetermined length is used to traverse the respiratory time series with a predetermined sliding step size. , cardiac time series , and blood flow time series The total length of the respiratory time series within each sliding window during the sliding process , cardiac time series , and blood flow time series , calculate the coupling strength from respiration to heart within the sliding window , coupling strength from respiration to blood flow , the coupling strength from heart to blood flow , and the coupling strength of blood flow to the heart , use formula 5 to calculate the respiratory-cardiac-blood flow coordination index RCH-SI corresponding to the sliding window; (Formula 5); Among them, the weight coefficients α, β, and γ are determined by the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and cardiac-blood flow bidirectional feedback regulation, respectively; The respiratory-cardiac-blood flow coordination index RCH-SI calculated in each sliding window together constitutes a respiratory-cardiac-blood flow coordination index sequence reflecting the dynamic changes of the plateau stress state. .

2. The method for dynamic quantitative assessment of plateau stress state by respiratory and cardiac blood flow coordination according to claim 1, characterized in that: Dynamic changes in respiratory-cardiac-blood flow coordination index sequence By performing mean and median statistics and fine composite multi-scale entropy complexity calculations respectively, we can obtain the overall index characterizing the plateau stress state.

3. The method for dynamic quantitative assessment of plateau stress state by respiratory and cardiac blood flow coordination according to claim 1, characterized in that: Corresponding to the respiratory-to-cardiac coupling strength within the sliding window , coupling strength from respiration to blood flow , coupling strength from heart to blood flow , and the coupling strength of blood flow to the heart , calculated according to formula 1, formula 2, formula 3, and formula 4; (Formula 1); Where p is the model order, is the state vector, is a 3×3 coefficient matrix, is a white noise vector; Perform discrete Fourier transform and calculate the frequency domain transfer function matrix ; (Formula 2); in, , for Elements in , represents the frequency domain transfer function of physiological signal j to physiological signal i; for , It represents the frequency domain transfer function of respiration to heart, reflecting the frequency-specific effect of respiration on the heart; It represents the frequency domain transfer function of the heart to blood pressure, reflecting the frequency-specific influence of the heart on blood pressure; is a 3×3 identity matrix; (Formula 3); It represents the directional influence of physiological signal j on physiological signal i at a specific frequency f, with a value range of 0-1; correspond Elements in , physiological signal j and physiological signal i represent respiratory, cardiac, or blood flow signals, and j is not equal to i; (Formula 4); in, is the lower limit frequency of physiological signal j, the lower limit frequency of respiratory signal is 0.1Hz, and the lower limit frequency of heart and blood flow signal is 0.003Hz; is the upper limit frequency of physiological signal j, the upper limit frequency of respiratory signal is 0.3Hz, and the upper limit frequency of heart and blood flow signal is 0.4Hz; is the directional coupling strength from physiological signal j to physiological signal i; When j is the respiratory signal and i is the cardiac signal, the coupling strength from respiration to heart is obtained. , j is the respiratory signal and i is the blood flow signal, and the coupling strength from respiration to blood flow is obtained , j is the heart signal and i is the blood flow signal, and the coupling strength from the heart to the blood flow is obtained , j is the blood flow signal i is the heart signal and the coupling strength of blood flow to the heart .

4. The method for dynamic quantitative assessment of plateau stress state by respiratory and cardiac blood flow coordination according to claim 1, characterized in that: In the signal preprocessing step, for the breath signal, a Butterworth bandpass filter with a cutoff frequency of 0.05-1.0 Hz was used to remove low-frequency motion artifacts and high-frequency ECG interference, and then a 50 Hz notch filter was used to suppress power frequency interference to obtain the preprocessed breath signal; for the ECG signal, baseline drift was removed by median filtering, power frequency interference was suppressed by a 50 Hz notch filter, and high-frequency noise was eliminated by a 0.5-100 Hz Butterworth bandpass filter to obtain the preprocessed ECG signal; for the CBP signal, baseline drift was removed by a Butterworth high-pass filter with a cutoff frequency of 0.5 Hz, motion artifacts were reduced by a Butterworth low-pass filter with a cutoff frequency of 5 Hz, and power frequency interference was eliminated by a 50 Hz notch filter to obtain a clean preprocessed CBP signal.

5. The method for dynamic quantitative assessment of plateau stress state by respiratory and cardiac blood flow coordination according to claim 1, characterized in that: In the signal processing step, when detecting the preprocessed ECG signal, the QRS complex wave detection algorithm QRS_Detection is used to detect the preprocessed ECG signal; when detecting the peak point of the systolic pressure of each cycle of the blood pressure waveform, the first-order derivative of the preprocessed CBP signal is calculated to identify the peak slope change point. When no valid peak is detected for 2 consecutive seconds, the detection threshold is automatically lowered, and the lower limit of the threshold is set to 0.4 times the average fluctuation range.

6. A device for dynamic quantitative assessment of plateau stress state by coordinated respiratory and cardiac blood flow, comprising: Signal preprocessing unit, signal processing unit, respiratory-cardiac-blood flow coordinated index sequence calculation unit; The signal preprocessing unit preprocesses the synchronous Breath, ECG, and CBP signals to obtain preprocessed Breath, ECG, and CBP signals; The signal processing unit detects the pre-processed ECG signal, locates the peak position of the QRS complex wave, and calculates the heart beat interval time series RR(n); for the pre-processed CBP signal, the peak point of the systolic pressure of each cycle of the blood pressure waveform is detected, and the systolic pressure time series is obtained based on all the detected continuous peak points. ; Preprocessed Breath, heart beat interval time series RR(n), systolic blood pressure time series , with frequency Resample to obtain the respiratory time series , cardiac time series , and blood flow time series ; The respiratory-cardiac-blood flow synergy index sequence calculation unit uses a sliding window of a predetermined length to traverse the respiratory time series with a predetermined sliding step size. , cardiac time series , and blood flow time series The total length of the respiratory time series within each sliding window during the sliding process , cardiac time series , and blood flow time series , calculate the coupling strength from respiration to heart within the sliding window , coupling strength from respiration to blood flow , coupling strength from heart to blood flow , and the coupling strength of blood flow to the heart , use formula 5 to calculate the respiratory-cardiac-blood flow coordination index RCH-SI corresponding to the sliding window; (Formula 5); Among them, the weight coefficients α, β, and γ are determined by the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and cardiac-blood flow bidirectional feedback regulation, respectively; The respiratory-cardiac-blood flow coordination index RCH-SI calculated in each sliding window together constitutes a respiratory-cardiac-blood flow coordination index sequence reflecting the dynamic changes of the plateau stress state. .

Images