8 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 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.

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.