Method for evaluating high altitude stress state by respiratory-heart blood flow synergic dynamic quantification and device thereof

Abstract

The application provides a respiration-heart-blood flow synergy analysis method based on synchronous continuous ECG, CBP and Breath signals, which continuously and dynamically quantifies the coupling strength of respiration-heart, respiration-blood flow and heart-blood flow, and comprehensively obtains a respiration-heart-blood flow synergy index (RCH-SI) for highland stress state evaluation. The method can effectively solve the limitations of traditional evaluation methods based on single physiological signal and occasional discrete physiological characteristics, overcome the defects of existing methods that cannot quantify the synergy of the three systems of respiration, heart and blood flow, improve the accuracy of highland stress state evaluation, and meet the continuous, dynamic and real-time monitoring requirements of highland stress state.

Description

Technical Field

[0001] This invention belongs to the field of high-altitude health monitoring technology, specifically involving a frequency band-specific coupling analysis method based on synchronous respiration (Breath), electrocardiogram (ECG), and continuous blood pressure (CBP, reflecting blood flow status) signals. It is used for dynamic quantification of respiratory-cardiac-blood flow synergistic compensation function in high-altitude (greater than 3000 meters) low-pressure and low-oxygen environments, and is suitable for early warning of acute mountain sickness (AMS) under high-altitude stress, high-altitude acclimatization assessment, and health management of high-altitude workers. Background Technology

[0002] With the implementation of the national strategy for building a modern country and the rapid development of transportation tools such as railways and aviation, more and more people from plains are frequently venturing to high-altitude areas to participate in economic construction, natural resource development, or to engage in commercial trade, competitive sports, and tourism. However, due to insufficient time to acclimatize to the low-oxygen environment of high altitudes, altitude stress occurs when people rapidly ascend to altitudes above 3000 meters. This manifests as a decrease in arterial blood oxygen partial pressure, triggering excitation of carotid body chemoreceptors, leading to a surge in ventilation, increased heart rate, and fluctuations in blood pressure, among other physiological changes. If compensation fails, it can develop into acute myeloid muscular dystrophy (AMS), the most common condition following high-altitude exposure (incidence rate 40%-90%), and may even worsen into high-altitude pulmonary edema or high-altitude cerebral edema. Therefore, accurate assessment of high-altitude stress is of great significance for early warning and prevention of AMS susceptibility.

[0003] Given the advantages of rapid, convenient, and non-invasive acquisition of human physiological signals, blood oxygen saturation (SpO2), ECG, Breath, blood pressure (BP), EEG, and body temperature are often used for monitoring, predicting, and warning of susceptibility to acute malignant syndrome (AMS). There is no significant gap between domestic and international research in terms of target physiological signals, monitoring methods, and analytical techniques. Most studies primarily use discrete SpO2 measured in-situ as the main indicator, with a few studies also focusing on heart rate, Breath, blood pressure, EEG, and electrical impedance. Analysis of heart rate variability mainly concentrates on traditional time-domain (e.g., SDNN, pNN50) and frequency-domain (e.g., LF, HF, LF / HF) characteristic parameters, rarely involving nonlinear indicators and joint parameters, and lacking methods and indicators for continuous dynamic assessment of comprehensive continuous multimodal physiological signals. In summary, existing research on AMS under high-altitude stress based on physiological signals often focuses only on single physiological signals, neglecting the holistic nature of the body and the interactions between multiple organs and systems. Furthermore, many studies obtain discrete characteristics of physiological signals through occasional measurements, lacking continuous monitoring data support and methods and indicators for continuous dynamic assessment of high-altitude stress status. Summary of the Invention

[0004] This invention provides a method for analyzing respiratory-cardio-hemodynamic synergy based on continuous ECG, BP, and Breath signals. By continuously and dynamically quantifying the intensity of respiratory-cardio-hemodynamic coupling and respiratory-hemodynamic bidirectional coupling, a comprehensive respiratory-cardio-hemodynamic synergy index (RCH-SI) is obtained for assessing high-altitude stress.

[0005] The method for dynamic quantitative assessment of high-altitude stress state involving respiratory, cardiac, and blood flow coordination in this application includes: Signal preprocessing steps, signal processing steps, and steps for calculating respiratory-cardiac-blood flow coordination index sequences; In the signal preprocessing step, the synchronization Breath, ECG, and CBP signals are preprocessed to obtain the preprocessed Breath, ECG, and CBP signals. In the signal processing step, the preprocessed ECG signal is detected, the peak position of the QRS complex is located, and the heart rate interval time series RR(n) is calculated; for the preprocessed CBP signal, the peak point of systolic blood pressure in each cycle of the blood pressure waveform is detected, and the systolic blood pressure time series is obtained based on all detected consecutive peak points. ; for preprocessed Breath, heart rate interval time series RR(n), and systolic blood pressure time series , in frequency Resampling was performed to obtain the respiratory time series. Cardiac time series and blood flow time series ; In the step of calculating the respiratory-cardiac-blood flow coordination index sequence, 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 full length; for 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 the 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. The respiratory-cardiac-blood flow synergy index RCH-SI corresponding to the sliding window is calculated using Equation 5. (Equation 5); Among them, the weighting coefficients α, β, and γ are determined through the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and bidirectional feedback regulation of cardiac-blood flow, respectively. The respiratory-cardiac-blood flow synergy index (RCH-SI) calculated within each sliding window together constitutes a respiratory-cardiac-blood flow synergy index sequence reflecting the dynamic changes of high-altitude stress. .

[0006] Preferably, the dynamically changing respiratory-cardiac-blood flow coordination index sequence By performing mean and median statistics and calculating the complexity of fine-grained multi-scale entropy values, we can obtain an overall index characterizing the stress state of the plateau.

[0007] Preferably, the coupling strength from respiration to the heart corresponds to 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. Calculations are performed according to Equations 1, 2, 3, and 4. (Equation 1); Where p is the model order, For state vectors, It is a 3×3 coefficient matrix. For white noise vector; Perform a discrete Fourier transform and calculate the frequency domain transfer function matrix; (Equation 2); in, The frequency domain transfer function matrix, for elements in , representing the frequency domain transfer function of physiological signal j to physiological signal i, specifically, for The first column is respiration, the second column is heart rate reflecting cardiac function, and the third column is blood pressure. This represents the frequency domain transfer function of respiration to the heart, reflecting the frequency-specific effect of respiration on the heart; This represents the frequency domain transfer function of the heart to blood pressure, reflecting the frequency-specific influence of the heart on blood pressure; It is a 3×3 identity matrix; (Equation 3); This represents the directional effect of physiological signal j on physiological signal i at a specific frequency f, with a value range of 0-1; correspond elements in Physiological signals j and i represent respiratory, cardiac, or blood flow signals, and j and i are not equal; (Equation 4); in, The lower limit frequency of physiological signal j is 0.1 Hz for respiratory signals and 0.003 Hz for cardiac and blood flow signals. The upper limit frequency of physiological signal j is 0.3 Hz for respiratory signals and 0.4 Hz for cardiac and blood flow signals. The directional coupling strength from physiological signal j to physiological signal i; When j represents the respiratory signal and i represents the cardiac signal, the coupling strength from respiration to the heart is obtained. The coupling strength between respiration and blood flow is obtained when j is the respiratory signal and i is the blood flow signal. When j represents the cardiac signal and i represents the blood flow signal, the coupling strength from the heart to the blood flow is obtained. The coupling strength from blood flow to the heart is obtained when j is the blood flow signal and i is the cardiac signal. .

[0008] Preferably, in the signal preprocessing step, for the Breath signal, a Butterworth bandpass filter with a cutoff frequency of 0.05-1.0Hz is used to remove low-frequency motion artifacts and high-frequency ECG interference, and a 50 Hz notch filter is used to suppress power line interference to obtain a preprocessed Breath signal; for the ECG signal, a median filter is used to remove baseline drift, a 50 Hz notch filter is used to suppress power line interference, and a 0.5-100 Hz Butterworth bandpass filter is used to eliminate high-frequency noise to obtain a preprocessed ECG signal; for the CBP signal, a Butterworth high-pass filter with a cutoff frequency of 0.5 Hz is used to remove baseline drift, a Butterworth low-pass filter with a cutoff frequency of 5 Hz is used to reduce motion artifacts, and a 50 Hz notch filter is used to eliminate power line interference to obtain a clean preprocessed CBP signal.

[0009] Preferably, in the signal processing step, when detecting the preprocessed ECG signal, the QRS composite wave detection algorithm QRS_Detection is used to detect the preprocessed ECG signal; when detecting the peak point of the systolic blood pressure of each cycle blood pressure waveform, the first derivative of the preprocessed CBP signal is calculated to identify the peak slope change point. When no effective peak is detected for 2 consecutive seconds, the detection threshold is automatically reduced, 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 respiratory, cardiac, and blood flow coordination in high-altitude stress states, as described in this application, comprises: Signal preprocessing unit, signal processing unit, and respiratory-cardiac-blood flow coordination 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 preprocessed ECG signal, locates the peak position of the QRS complex, and calculates the heartbeat interval time series RR(n). For the preprocessed CBP signal, it detects the peak point of systolic blood pressure in each cycle of the blood pressure waveform and obtains the systolic blood pressure time series based on all detected consecutive peak points. ; for preprocessed Breath, heart rate interval time series RR(n), and systolic blood pressure time series , in frequency Resampling was performed to obtain the respiratory time series. Cardiac time series and blood flow time series ; The respiratory-cardiac-blood flow coordination index sequence calculation unit uses a sliding window of predetermined length to traverse the respiratory time series with a predetermined sliding step size. Cardiac time series and blood flow time series The full length; for 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 the 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. The respiratory-cardiac-blood flow synergy index RCH-SI corresponding to the sliding window is calculated using Equation 5. (Equation 5); Among them, the weighting coefficients α, β, and γ are determined through the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and bidirectional feedback regulation of cardiac-blood flow, respectively. The respiratory-cardiac-blood flow synergy index (RCH-SI) calculated within each sliding window together constitutes a respiratory-cardiac-blood flow synergy index sequence reflecting the dynamic changes of high-altitude stress. .

[0011] The method proposed in this application can effectively solve the limitations of traditional assessment methods based on single-modal physiological signals and occasional discrete physiological characteristics, overcome the shortcomings of existing methods in quantifying the synergistic effects of the respiratory, cardiac, and circulatory systems, improve the accuracy of high-altitude stress assessment, and meet the needs for continuous, dynamic, and real-time monitoring of high-altitude stress. Attached Figure Description

[0012] Figure 1 This is a flowchart of the method for dynamic quantitative assessment of high-altitude stress state by respiratory, cardiac and blood flow coordination according to this application.

[0013] Figure 2 This is a schematic diagram of the original Breath, ECG, and CBP signals.

[0014] Figure 3 This is a schematic diagram of the preprocessed Breath, ECG, and CBP signals and their locally amplified signals.

[0015] Figure 4 This is a schematic diagram showing the CBP feature point wave detection results and local magnification effect of the preprocessed ECG signal.

[0016] Figure 5 To resample to the same fs A schematic diagram of the subsequent B(t), R(t), and S(t) sequences.

[0017] Figure 6 This is a schematic diagram of the respiratory-cardiac-blood flow coordination index (RCH-SI) calculated with a window width of 15s and a step size of 1s.

[0018] Figure 7 This is a schematic diagram of the dynamic sequence of respiratory-cardiac-blood flow synergy index (RCH-SI(t)) before, 24 hours after, and 72 hours after the rapid ascent to high altitude.

[0019] Figure 8 This is a schematic diagram showing the mean, median, and complexity of the dynamic sequence of respiratory-cardiac-blood flow synergy index RCH-SI(t) before, 24 hours after, and 72 hours after the rapid ascent to high altitude. Detailed Implementation

[0020] The present application will now be described in detail with reference to the accompanying drawings.

[0021] 1. Synchronous preprocessing of Breath, ECG, and CBP signals 1) 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. Then, a 50 Hz notch filter is used to suppress power frequency interference, resulting in 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 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 continuous blood pressure waveform.

[0024] 2. Feature Point Detection and Processing 1) ECG signal QRS peak detection: The QRS complex detection algorithm QRS_Detection in the open-source ECG signal processing toolbox ECGdeli was used to detect the preprocessed ECG signal, locate the QRS complex peak, and then calculate the heartbeat interval time series. .

[0025] 2) CBP Signal Peak Detection: Based on first-order differential analysis and an adaptive threshold method, the peak point SBP (corresponding to systolic blood pressure) of each blood pressure waveform cycle is automatically identified. 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). When no valid peak is detected for 2 consecutive seconds, the detection threshold is automatically lowered. A lower threshold limit (0.4 × average fluctuation range) is set to prevent oversensitivity. The systolic blood pressure time series is obtained based on all detected consecutive CBP peak points. .

[0026] 3. Resampling The preprocessed Breath signal and the calculated heartbeat interval time series Systolic blood pressure time series Resampling is performed at all frequencies. They respectively represent respiration, heart, and blood flow. , , Time series.

[0027] 4. Dynamic Window Sliding: Set the sliding window length to T, and the sliding step size to 1 second (adjustable). Iterate through this window. , , The entire length, for each window during the sliding process. , , The time series is processed in step 5. 5. Quantifying respiratory-cardiac-blood flow coordination indicators 1) Constructing a respiratory-cardiac-blood flow model: for , , The three time series were fitted with a multivariate autoregressive model according to Equation 1 to obtain the coefficient matrix. k=1,...,p (p is the model order).

[0028] (Equation 1); in, For state vectors, It is a 3×3 coefficient matrix. This is a white noise vector.

[0029] 2) Calculate the transfer function matrix in the frequency domain. : Using Fourier transform to analyze data based on respiration, heart rate, and blood flow , , The time series model is transformed into the frequency domain, yielding the transfer function matrix, as shown in Equation 2: (Equation 2); in, The frequency domain transfer function matrix, for elements in , representing the frequency domain transfer function of physiological signal j to physiological signal i, specifically, for The first column is respiration, the second column is heart rate reflecting cardiac function, and the third column is blood pressure. This represents the frequency domain transfer function of respiration to the heart, reflecting the frequency-specific effect of respiration on the heart; This represents the frequency domain transfer function of the heart to blood pressure, reflecting the frequency-specific influence of the heart on blood pressure. It is a 3×3 identity matrix; 3) Calculation of directional effects: The directional coupling strength from one physiological signal to another is quantified using the partial directional coherence method, as shown in Equation 3: (Equation 3); in, The directional effect of physiological signal j on physiological signal i at a specific frequency f, with a value range of (0-1). correspond elements in Physiological signals j and i represent respiratory, cardiac, or blood flow signals, and j and i are not equal.

[0030] 4) Coupling strength calculation: Calculating coupling strength in specific frequency bands with physiological significance. The total coupling strength in each direction is obtained by integration, as shown in Equation 4: (Equation 4); in, The lower limit frequency of physiological signal j is 0.1 Hz for respiratory signals and 0.003 Hz for cardiac and blood flow signals. The upper limit frequency of physiological signal j is 0.3 Hz for respiratory signals and 0.4 Hz for cardiac and blood flow signals. The directional coupling strength from physiological signal j to physiological signal i is given. Existing research indicates that respiration has a driving effect, meaning that respiration affects heart rhythm (respiratory sinus arrhythmia) and vascular tone (blood pressure changes) through mechano-neural mechanisms (such as changes in intrathoracic pressure and vagal nerve activity). Furthermore, a feedback mechanism exists between the heart and blood flow; the pressure reflex affects blood pressure through changes in heart rate, and conversely, changes in blood pressure can regulate heart rate, reflecting the dynamic balance of the autonomic nervous system. Therefore, this invention focuses on the unidirectional coupling from respiration to the heart, the unidirectional coupling from respiration to blood flow, and the bidirectional coupling from the heart to blood flow. Specifically, the coupling strength from respiration to the heart is obtained when j is the respiratory signal and i is the cardiac signal. The coupling strength between respiration and blood flow is obtained when j is the respiratory signal and i is the blood flow signal. When j represents the cardiac signal and i represents the blood flow signal, the coupling strength from the heart to the blood flow is obtained. The coupling strength from blood flow to the heart is obtained when j is the blood flow signal and i is the cardiac signal. .

[0031] 5) Define the respiratory-cardiac-blood flow coordination index: Based on the multi-directional total coupling strength obtained in step 4), calculate the respiratory-cardiac-blood flow coordination index RCH-SI according to Equation 5.

[0032] (Equation 5); The weighting coefficients α, β, and γ were determined through the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and bidirectional feedback regulation of cardiac-blood flow, respectively. Specifically, the weight α (0.4-0.5) for respiratory-cardiac coupling is the highest, stemming from the strong physiological association with respiratory sinus arrhythmia. Vagal nerve endings are directly distributed in the sinoatrial node, and respiratory movements achieve millisecond-level heart rate regulation through the pulmonary stretch receptor-vagal nerve reflex. Experimental data show that RSA contributes more than 60% of heart rate variability at rest. The weight β (0.2-0.3) for respiratory-blood flow coupling is the second lowest, because respiration indirectly affects blood flow, mainly through two pathways: one is the venous return fluctuation caused by changes in intrathoracic pressure (mechanical effect), and the other is the modulation of vascular sympathetic tone by respiratory rhythm (neural effect). The latter has a delay of about 0.5 seconds and its effect is weaker than that of RSA. The bidirectional coupling weight γ (0.3-0.4) between the heart and blood flow reflects the baroreflex closed loop. The baroreflex exhibits bidirectional characteristics; baroreceptors sense changes in arterial blood pressure and trigger cardiovascular regulatory mechanisms through neural conduction. When blood pressure rises, the afferent signal from the baroreceptors increases, inhibiting sympathetic nerve activity and increasing parasympathetic tone through neural circuits, thereby reducing heart rate and peripheral resistance and causing blood pressure to drop. Conversely, when blood pressure drops, the opposite mechanism causes blood pressure to rise.

[0033] The linear model was chosen primarily because it considers the hierarchical integration of respiration's driving effect on the cardiac and vascular systems and the closed-loop feedback within the cardiovascular system, which aligns with the physiological cascade characteristic of "respiration-led → cardiovascular-executed". The weight allocation 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 the RCH-SI within each sliding window constructs a sequence of respiratory-cardiac-blood flow co-indicators reflecting the dynamic changes in high-altitude stress. Further research is possible. Take the mean separately ( ), median ( ) statistical and fine composite multiscale entropy values ​​( The complexity calculation of ); the mean includes: ( The median includes: Statistical and refined composite multiscale entropy values ​​include: , , , , , We obtained overall indicators characterizing the stress state at high altitudes.

[0035] Example Breath, ECG, and CBP signals were collected from subjects before, 24 hours after, and 72 hours after rapid ascent to high altitude (3400 meters). The respiratory-cardiac-vascular synergy index (RCH-SI) was extracted at different time points during this ascent. Results showed that the RCH-SI was significantly higher 24 hours after ascent than before, but returned to pre-ascent levels after 72 hours. This indicates that the synergy of the respiratory, cardiac, and vascular systems is compensatorily enhanced in the short term after rapid ascent; that is, the three systems work synergistically through neuro-humoral regulatory mechanisms to jointly cope with the challenges of low-pressure, low-oxygen environments. This enhanced synergy is the core mechanism of the body's acute adaptation to low pressure and low oxygen. Figure 8 As shown, due to physiological compensation caused by altitude stress, the mean respiratory-cardiac-vascular coordination index of the subjects 24 hours after rapid ascent to high altitude was... Median All showed a significant increase, recovering after 72 hours, while complexity... The levels 24 hours and 72 hours after rapid ascent to high altitude were higher than before, indicating that the subjects entered a homeostatic state under high altitude stress, and the respiratory-cardiac-blood flow synergy was constantly changing dynamically.

[0036] This invention proposes a method for frequency-band specific coupling analysis of respiratory-cardiac-blood flow synergy based on synchronized ECG, CBP, and Breath signals, and its application in assessing high-altitude stress. This method achieves multi-dimensional coupling analysis through simultaneous quantification of three pathways: respiration to heart, respiration to blood flow, and bidirectional cardiac-blood flow regulation. PDC analysis technology further clarifies the coupling direction, and the respiratory-cardiac-blood flow synergy index sequence is further refined. It has dynamic characteristics, which can meet the requirements of continuous dynamic real-time monitoring of high-altitude stress, and is suitable for early warning of AMS under high-altitude stress, high-altitude acclimatization assessment and health management of high-altitude workers.

Claims

1. A device for dynamic quantitative assessment of high-altitude stress status through coordinated respiratory, cardiac, and blood flow responses, comprising: Signal preprocessing unit, signal processing unit, and respiratory-cardiac-blood flow coordination 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 preprocessed ECG signal, locates the peak position of the QRS complex, and calculates the heartbeat interval time series RR(n). For the preprocessed CBP signal, it detects the peak point of systolic blood pressure in each cycle of the blood pressure waveform and obtains the systolic blood pressure time series based on all detected consecutive peak points. ; for preprocessed Breath, heart rate interval time series RR(n), and systolic blood pressure time series , in frequency Resampling was performed to obtain the respiratory time series. Cardiac time series and blood flow time series ; The respiratory-cardiac-blood flow coordination index sequence calculation unit uses a sliding window of predetermined length to traverse the respiratory time series with a predetermined sliding step size. Cardiac time series and blood flow time series The full length; for 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 the 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. The respiratory-cardiac-blood flow synergy index RCH-SI corresponding to the sliding window is calculated using Equation 5. (Equation 5); Among them, the weighting coefficients α, β, and γ are determined through the physiological mechanisms of respiratory-cardiac coupling, respiratory-blood flow coupling, and bidirectional feedback regulation of cardiac-blood flow, respectively. The respiratory-cardiac-blood flow synergy index (RCH-SI) calculated within each sliding window together constitutes a respiratory-cardiac-blood flow synergy index sequence reflecting the dynamic changes of high-altitude stress. .

2. The device for dynamic quantitative assessment of high-altitude stress state through coordinated respiratory, cardiac, and blood flow as described in claim 1, characterized in that: Dynamically changing respiratory-cardiac-blood flow synergy index sequence By performing mean and median statistics and calculating the complexity of fine-grained multi-scale entropy values, we can obtain an overall index characterizing the stress state of the plateau.

3. The device for dynamic quantitative assessment of high-altitude stress state through coordinated respiratory, cardiac, and blood flow as described in claim 1, characterized in that: The coupling strength from respiration to the 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. Calculations are performed according to Equations 1, 2, 3, and 4. (Equation 1); Where p is the model order, For state vectors, It is a 3×3 coefficient matrix. For white noise vector; Perform a discrete Fourier transform and calculate the frequency domain transfer function matrix. ; (Equation 2); in, , for elements in , representing the frequency domain transfer function of physiological signal j to physiological signal i; for , This represents the frequency domain transfer function of respiration to the heart, reflecting the frequency-specific effect of respiration on the heart. This represents the frequency domain transfer function of the heart to blood pressure, reflecting the frequency-specific effect of the heart on blood pressure. It is a 3×3 identity matrix; (Equation 3); This represents the directional effect of physiological signal j on physiological signal i at a specific frequency f, with a value range of 0-1; correspond elements in Physiological signals j and i represent respiratory, cardiac, or blood flow signals, and j and i are not equal; (Equation 4); in, The lower limit frequency of physiological signal j is 0.1 Hz for respiratory signals and 0.003 Hz for cardiac and blood flow signals. The upper limit frequency of physiological signal j is 0.3 Hz for respiratory signals and 0.4 Hz for cardiac and blood flow signals. The directional coupling strength from physiological signal j to physiological signal i; When j represents the respiratory signal and i represents the cardiac signal, the coupling strength from respiration to the heart is obtained. The coupling strength between respiration and blood flow is obtained when j is the respiratory signal and i is the blood flow signal. When j represents the cardiac signal and i represents the blood flow signal, the coupling strength from the heart to the blood flow is obtained. The coupling strength from blood flow to the heart is obtained when j is the blood flow signal and i is the cardiac signal. .

4. The device for dynamic quantitative assessment of high-altitude stress state by respiratory, cardiac, and blood flow coordination according to claim 1, characterized in that: In the signal preprocessing unit, for the Breath signal, a Butterworth bandpass filter with a cutoff frequency of 0.05-1.0Hz is used to remove low-frequency motion artifacts and high-frequency ECG interference, and a 50 Hz notch filter is used to suppress power line interference, resulting in a preprocessed Breath signal. For the ECG signal, a median filter is used to remove baseline drift, a 50 Hz notch filter is used to suppress power line interference, and a 0.5-100Hz Butterworth bandpass filter is used to eliminate high-frequency noise, resulting in a preprocessed ECG signal. For the CBP signal, a Butterworth high-pass filter with a cutoff frequency of 0.5Hz is used to remove baseline drift, a Butterworth low-pass filter with a cutoff frequency of 5Hz is used to reduce motion artifacts, and a 50Hz notch filter is used to eliminate power line interference, resulting in a clean preprocessed CBP signal.

5. The device for dynamic quantitative assessment of high-altitude stress state by respiratory, cardiac, and blood flow coordination according to claim 1, characterized in that: In the signal processing unit, when detecting the preprocessed ECG signal, the QRS composite wave detection algorithm QRS_Detection is used to detect the preprocessed ECG signal; when detecting the peak point of the systolic blood pressure waveform in each cycle, the first derivative of the preprocessed CBP signal is calculated to identify the point of change in the peak slope. When no effective 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.

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