14 results about "Cardiopulmonary coupling" patented technology

The invention provides a cardiac postoperative respiratory function monitoring system and method. The method comprises the steps that photoelectric volume pulse wave signals of a patient during cardiac postoperative breathing are collected through an intelligent optical sensor; extracting a heart rate variability signal of the patient, and determining a quantitative index of respiratory sinus arrhythmia of the patient through the heart rate variability signal; determining the arterial pressure variability of the patient according to the arterial pressure waveform, and fusing the arterial pressure variability and the quantitative index of the respiratory sinus arrhythmia to obtain the cardiopulmonary coupling strength; according to the cardiopulmonary coupling strength and the shallow and fast breathing index of the patient, combining a pre-trained failure prediction model during breathing machine withdrawal, outputting a failure risk value during machine withdrawal of the patient; and outputting a machine withdrawal decision support report of the patient according to the failure risk value and a preset individualized early warning threshold value. According to the scheme, the cardiac postoperative withdrawal risk can be evaluated based on the dynamic cooperation mechanism between the cardiovascular system and the respiratory system.

The present invention discloses a cardiopulmonary coupling quantification method and system based on multimodal coupling analysis, which relates to the technical field of electrocardiogram signal processing. The method comprises the following steps: collecting electrocardiogram (ECG) signals and respiratory signals of a subject; extracting a heart beat interval (R-R) interval time series from the collected ECG signals; decomposing the R-R interval time series and the respiratory signal using variational mode decomposition to obtain intrinsic mode functions (IMFs) of the two time series; selecting the IMF with the largest power as the dominant IMF among all IMFs of the respiratory signal; selecting the IMF with the frequency matching the dominant IMF of the respiratory signal among the IMFs of the R-R signal; calculating the synchronization index between the dominant IMF of the respiratory signal and the frequency matching IMF of the R-R sequence by using the instantaneous phase difference between the two; and finally, quantifying the instantaneous magnitude of RSA by calculating the power of the frequency matching IMF of the R-R interval sequence during its strong synchronization period.

The invention provides a non-contact cardiopulmonary coupling analysis method and system based on millimeter wave perception. The method comprises the steps that millimeter wave frequency modulation signals are continuously sent to a subject, initial echo signals are received and converted to obtain converted echo signals, and the distance position to a target is positioned from the converted echo signals; calculating a first phase for the conversion echo signal at each time point of the distance position of the target, calculating a second phase based on each time point and the first phase at a time point before the time point, and calculating a radial displacement signal based on the second phase; calculating a breathing displacement signal and a heartbeat displacement signal based on the radial displacement signal, calculating a breathing instantaneous phase of each time point based on the breathing displacement signal, and selecting at least one target time period based on the breathing instantaneous phase; a heartbeat accompanying change value of the target time period is calculated based on the heartbeat displacement signal of the target time period, and a modulation index is calculated based on the heartbeat accompanying change values of all the target time periods.

The invention relates to a training aircraft pilot physiological state monitoring and early warning system, and belongs to the technical field of aviation physiological monitoring and early warning. The system comprises a data acquisition and preprocessing module, an autonomic nervous system toughness evaluation module, a dynamic toughness index fusion module and an early warning generation module. The system obtains a multi-mode physiological signal and a flight parameter of a pilot; by calculating a cross-modal phase synchronization index, a state space trajectory convergence rate and a brain function network modularization coefficient, evaluation is carried out from three dimensions of cardiopulmonary coupling strength, system recovery capability and cognitive load state; carrying out weighted fusion on the multi-dimensional indexes to form a comprehensive dynamic toughness index, and determining an early warning grade according to the comprehensive dynamic toughness index; according to the method, comprehensive evaluation indexes are constructed, so that the defect that a traditional method depends on a single physiological index and is easily interfered is overcome, the false alarm rate is remarkably reduced, and the robustness and reliability of an early warning result are enhanced.

The present disclosure provides systems and methods of analyzing a sleep session as a REM sleep session or a wake session. According to aspects of the present disclosure, a computer- implemented method includes accessing cardiopulmonary coupling data throughout a sleep session of a person, identifying a period in the sleep session corresponding to very low frequency coupling in the cardiopulmonary coupling data, accessing high frequency coupling data and / or low frequency coupling data in the cardiopulmonary coupling data corresponding to the period, and designating the period as a REM sleep session or a wake session based on the high frequency coupling data and / or the low frequency coupling data corresponding to the period, wherein the period is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

This invention discloses an emotion monitoring system based on sleep HRV characteristics, belonging to the field of intelligent health monitoring technology. The system includes: simultaneously collecting PPG, EEG, and near-infrared light signals during sleep; analyzing HRV sequences and respiratory wave morphology based on PPG signals; achieving sleep microstructure staging through cardiopulmonary coupling analysis; extracting dynamic HRV features based on the staging results and comparing them with personalized stage baselines; detecting and labeling emotional load events; constructing and updating a nighttime emotional event atlas; predicting safe intervention time windows based on the atlas and sleep staging; executing personalized interventions and collecting physiological feedback data to form a closed-loop optimization system. This invention achieves high temporal resolution, event-oriented nighttime emotional dynamic identification and safe intervention, improving the accuracy and reliability of emotion monitoring.

The invention discloses an emotion recognition method based on electrocardio and breathing bimodal signals. The method comprises the following steps: firstly, acquiring ECG and RESP signals in different emotional states, preprocessing the signals, and constructing an emotion recognition data set; then, ECG signal features and RESP signal features are extracted; an emotion recognition model is constructed, the time domain, the frequency domain and the nonlinear features of the ECG signals are encoded through three parallel encoders, and the respiratory frequency, the respiratory depth and the cardiopulmonary coupling features are also encoded through the three parallel encoders; and finally, performing boundary smoothing on the emotion feature vector output by the emotion recognition model by adopting a Markov model, and dividing an emotion state according to the emotion feature vector after boundary smoothing. According to the method, an ECG-RESP bimodal fusion framework is established, and on the basis of fully fusing ECG and RESP signals, boundary fuzzification is performed on emotional states, so that the recognition accuracy of similar emotions is improved.

The invention provides a dual-heart health early warning system and method based on millimeter wave radar and electronic equipment, and relates to the technical field of non-contact physiological signal processing, the system comprises a signal acquisition and processing module used for extracting a target respiration signal and a target heartbeat signal in an original echo signal; the multi-parameter adaptive classification module is used for carrying out heart function parameter extraction processing to obtain a target respiratory rate, a target heart rate, target heart rate variability and target cardiopulmonary coupling; the psychological and emotion evaluation module is used for obtaining a target psychological state evaluation result and a target emotion state evaluation result; and the fusion decision module is used for inputting the target respiratory rate, the target heart rate, the target heart rate variability, the target cardiopulmonary coupling, the target psychological state evaluation result and the target emotional state evaluation result into the double-heart linkage early warning model to obtain a physiological parameter early warning result. According to the method, physiological signals with higher precision are extracted, and effective early warning of psychological and heart parameters is realized.

The application provides a signal processing method and related device, comprising: obtaining a breathing signal and a heartbeat signal of a target object; determining an instantaneous heart rate signal of the target object according to the heartbeat signal; determining a cardiopulmonary coupling degree index of the target object based on the breathing signal and the instantaneous heart rate signal, the cardiopulmonary coupling degree index being used to identify the degree of respiratory sinus arrhythmia of the target object. Through the above method, the breathing signal and the heartbeat signal of the target object can be comprehensively analyzed, and the cardiopulmonary coupling degree index used to identify the degree of respiratory sinus arrhythmia of the target object can be obtained from the perspective of cardiopulmonary coupling, so that the respiratory sinus arrhythmia of the target object can be more accurately analyzed.

The present invention discloses a portable autonomic nervous function assessment device, method, equipment, and program product. Specifically, the device utilizes an intelligent respiratory monitoring and guidance program to individually adjust a user's breathing to a specific frequency, utilizes a heart rate acquisition device to record the user's heart rate fluctuations during this process, quantitatively analyzes the user's cardiopulmonary coupling strength based on a feature extraction algorithm, and constructs a mapping model between physiological characteristics and clinical indicators to achieve quantitative assessment and portable monitoring of autonomic nervous function. Based on accurate decoding of the cardiopulmonary coupling process, the present invention realizes a portable autonomic nervous function assessment device, method, equipment, and program product that is fast, inexpensive, easy to operate, and has widespread application. The device requires a simple measurement device and can be used for personalized home health assessments.

The present invention discloses a cardiopulmonary coupling feature extraction method and system based on adaptive Fourier decomposition. The method can take into account the dynamics of electrophysiological signals, extract the spectral features of electrophysiological signals and realize the adaptive decomposition of key rhythms. The specific steps are as follows: preprocessing the collected original single-lead electrocardiogram (ECG) signal and respiratory signal; extracting the heart beat interval R-R interval time series from the preprocessed ECG signal; using the adaptive bandwidth Fourier decomposition algorithm to process the respiratory signal time series and the R-R interval time series respectively to obtain the Fourier natural frequency band functions of the above two time series; obtaining the Fourier decomposition coefficients from the analytical form of the Fourier natural frequency band functions of the two time series at different frequencies, and calculating the CPC value based on the Fourier decomposition coefficients of the R-R and respiratory signal time series to construct a cardiopulmonary coupling map; extracting multiple types of cardiopulmonary coupling features based on the cardiopulmonary coupling map.

This invention provides a non-contact cardiopulmonary coupling analysis method and system based on millimeter-wave sensing. The method includes the following steps: continuously sending a millimeter-wave frequency-modulated signal towards the subject, receiving an initial echo signal, and converting it to obtain a converted echo signal; locating the distance position to a target from the converted echo signal; calculating a first phase for the converted echo signal at each time point of the target's distance position; calculating a second phase based on the first phase at each time point and the time point preceding that time point; calculating a radial displacement signal based on the second phase; calculating a respiratory displacement signal and a heartbeat displacement signal based on the radial displacement signal; calculating an instantaneous respiratory phase at each time point based on the respiratory displacement signal; selecting at least one target time period based on the instantaneous respiratory phase; calculating the accompanying heartbeat variation value for the target time period based on the heartbeat displacement signal for the target time period; and calculating a modulation index based on the accompanying heartbeat variation values ​​for all target time periods.

The invention discloses a human body blood pressure simulation system and method based on cardiopulmonary coupling, and belongs to the technical field of physiological parameter simulation equipment. The system comprises a rack, a double-eccentric rotating assembly, an independent motor, a pressure output component and a selectable baseline adjusting mechanism. The two independent motors respectively drive a first rotating component and a second rotating component in the double-eccentric rotating component, so that the first rotating component and the second rotating component are superposed to displace and are transmitted to the water tank through the test panel to drive a water column to lift, thereby forming composite water pressure with heartbeat and breathing characteristics; the base line adjusting mechanism can apply a constant pressure base line on the basis; the pressure output component receives and outputs the composite water pressure. The multi-frequency characteristic of the arterial pressure of the human body can be accurately reproduced, the problem of simulation distortion of a traditional single pressure source is solved, and the method is suitable for fatigue test and performance evaluation of blood pressure testing equipment.

The present disclosure provides systems and method of analyzing whether a sleep epoch is a REM sleep epoch or a wake epoch. In accordance with aspects of the present disclosure, a computer-implemented method includes accessing cardiopulmonary coupling data spanning a sleep period for a person, identifying an epoch in the sleep period corresponding to very-low frequency coupling in the cardiopulmonary coupling data, accessing high-frequency coupling data and / or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch, and designating the epoch as a REM sleep epoch or as a wake epoch based on the high-frequency coupling data and / or the low-frequency coupling data corresponding to the epoch, where the epoch is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.