30 results about "Cardiopulmonary coupling" patented technology

An assessment of sleep quality and sleep disordered breathing is determined from the cardiopulmonary coupling between two physiological data series. In an embodiment, an R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal (NN) interval series. The amplitude variations in the QRS complex are used to extract to a surrogate respiration signal (i.e., ECG-derived respiration (EDR)) that is associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross power. Using the appropriate thresholds for the coherent cross power, the proportion of sleep spent in CAP, non-CAP, and wake and / or REM are determined.

An assessment of sleep quality and sleep disordered breathing is determined from cardiopulmonary coupling between two physiological data series. An R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal interval series. The amplitude variations in the QRS complex are used to extract a surrogate respiration signal (i.e., ECG-derived respiration) associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross-power. Using the appropriate thresholds for the coherent cross-power, the proportion of sleep spent in CAP, non-CAP, and wake and / or REM are determined. Coherent cross-power can be applied to differentiate obstructive from non-obstructive disease, and admixtures of the same.

An assessment of sleep quality and sleep disordered breathing is determined from cardiopulmonary coupling between two physiological data series. An R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal interval series. The amplitude variations in the QRS complex are used to extract a surrogate respiration signal (i.e., ECG-derived respiration) associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross-power. Using the appropriate thresholds for the coherent cross-power, the proportion of sleep spent in CAP, non-CAP, and wake and / or REM are determined. Coherent cross-power can be applied to differentiate obstructive from non-obstructive disease, and admixtures of the same.

An assessment of sleep quality and sleep disordered breathing is determined from the cardiopulmonary coupling between two physiological data series. In an embodiment, an R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal (NN) interval series. The amplitude variations in the QRS complex are used to extract to a surrogate respiration signal (i.e., ECG-derived respiration (EDR)) that is associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross power. Using the appropriate thresholds for the coherent cross power, the proportion of sleep spent in CAP, non-CAP, and wake and / or REM are determined.

A cardiopulmonary coupling analysis method based on single lead ECG includes the steps of extracting an RR inter-phase sequence and an EDR signal from body surface single-lead ECG signals, eliminating the noise components of the EDR signal according to the relevance between the RR inter-phase sequence and the EDR signal by means of a self-adaptive filter with the RR inter-phase sequence as the reference input signal of the self-adaptive filter and the EDR signal as the ideal signal of the self-adaptive filter to obtain an enhanced type EDR signal, conducting experience pattern decomposing on the RR inter-phase sequence and the enhanced type EDR signal to obtain a plurality of intrinsic mode functions of the RR inter-phase sequence and a plurality of intrinsic mode functions of the enhanced type EDR signal, and selecting the intrinsic mode function, corresponding to a preset frequency band, of the RR inter-phase sequence and the intrinsic mode function, corresponding to the preset frequency band, of the enhanced type EDR signal for cardiopulmonary coupling analysis to obtain a coupling result.

The invention provides a sleep staging method based on a BCG (ballistocardiogram) signal. The method comprises the steps as follows: extracting a heart rate signal, a pulse signal and a respiration signal from the BCG signal acquired in advance, and calculating heart rate variability (HRV) and cardiopulmonary coupling (CPC) power spectrum according to the heart rate signal, the pulse signal and the respiration signal; acquiring change trend signals of frequency domain characteristics of the HRV and the CPC power spectrum, converting the change trend signals of the frequency domain characteristics of the HRV and the CPC power spectrum into multichannel image characteristics, performing characteristic extraction of the HRV and the CPC power spectrum, and extracting characteristic vectors according to time domain signals of the extracted HRV and CPC power spectrum; and training sleep staging results corresponding to HRV and CPC power spectrum density signals according to the multichannelimage characteristics and characteristic vectors. The sleep staging method combines the HRV and the CPC power spectrum to distinguish sleep stages and has higher adaptability and accuracy.

The present invention relates to a sleep quality monitoring system and method based on a magnetocardiogram. The monitoring method includes: S10, collecting the magnetocardiogram signal and the blood oxygen signal of the sleep time of the target user by means of the monitoring system; S20, preprocessing the magnetocardiogram signal and noise reduction processing, screening the available cardiomagnetic signal time period and obtaining the intermediate cardiomagnetic signal; S30, using the QRS wave detection method and the cardiopulmonary coupling algorithm to extract the intermediate cardiomagnetic signal, and obtaining the characteristic signal used to reflect the sleep quality of the target user; S40. Perform preprocessing and eigenvalue extraction on the blood oxygen signal and use a pre-trained machine learning model to screen the time period that can reflect the respiratory event of the target user; S50. Based on the eigenvalue and characteristic signal of the blood oxygen signal within the filtered time period , to obtain the sleep quality evaluation result of the target user. The above method can time-sequentially monitor respiratory events, and improve detection accuracy and reliability.

The present invention provides a system and method for phase synchronization analysis of the cardiopulmonary system, which is used to evaluate the degree of synchronization between heart rate and respiration, that is, the cardiopulmonary coupling strength; the system includes an ECG signal acquisition module, an ECG signal preprocessing module, ECG signal R peak detection module, respiratory signal acquisition module, respiratory signal preprocessing module, respiratory signal peak detection module and phase synchronization analysis module; wherein, the ECG signal acquisition module collects ECG signals; the ECG signal R peak detection module adopts The R wave detection algorithm based on wavelet transform detects the R wave peak; the respiratory signal acquisition module collects the respiratory signal; the respiratory signal peak detection module uses the threshold method to detect the respiratory peak point; the phase synchronization analysis module calculates the cardiopulmonary synchronization time based on the phase synchronization diagram as the cardiopulmonary coupling strength quantitative indicators.