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 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.

The invention provides a system and method for phase synchronization analysis of a cardiopulmonary system. The system and method are used for evaluating the synchronization degree of the heart rate and respiration, namely cardiopulmonary coupling strength. The system comprises an electrocardio signal collecting module, an electrocardio signal preprocessing module, an electrocardio R wave crest detection module, a respiration signal collecting module, a respiration signal preprocessing module, a respiration signal wave crest detection module and a phase synchronization analyzing module. The electrocardio signal collecting module is used for collecting electrocardio signals, the electrocardio R wave crest detection module detects an R wave crest by the adoption of an R wave detection algorithm based on wavelet transform, the respiration signal collecting module is used for collecting respiration signals, the respiration signal wave crest detection module is used for detecting a respiration wave crest point by the adoption of a threshold method, and the phase synchronization analyzing module is used for calculating cardiopulmonary synchronization time on the basis of a phase synchronization graph to serve as a quantitative index of the cardiopulmonary coupling strength.

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.

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 invention provides a sleep staging method based on electrocardiogram signals. The method comprises the steps that original electrocardiogram signals are acquired, and the original electrocardiographic signals are denoised to obtain the electrocardiogram signals; R points and RP intervals are acquired from the electrocardiogram signals, and a respiratory waveform signal is obtained according tothe R points and the RR intervals; according to the electrocardiogram signals and the respiratory waveform signal, electrocardiogram signal features, respiratory signal features and cardiopulmonary coupling features are acquired; the electrocardiogram signal features, the respiratory signal features and the cardiopulmonary coupling features are subjected to feature selection; according to the selected electrocardiogram signal features, respiratory signal features and cardiopulmonary coupling features, a machine learning algorithm is adopted for sleep staging. The accuracy of sleep staging based on the electrocardiogram signals is improved by introducing electrocardiogram signal complexity features in the electrocardiogram signal features.

The invention discloses a quantitative cardiopulmonary system interaction analysis method which includes the steps: acquiring multi-lead physiological signals of a subject in nocturnal sleep; marking sleep stages of the signals and extracting an RR interval sequence, a PP interval sequence, a PTT sequence and an RA interval sequence; synchronously segmenting the four sequences according to 5-minute duration in the same sleep state and then extracting three characteristic parameters: low-frequency Shannon entropy, high-frequency Shannon entropy and transfer entropy; training a neural network, determining neural network parameters and building a neural network cardiopulmonary interaction evaluation model; evaluating the cardiopulmonary function of the subject by the trained neural network and outputting evaluation results. Cardiopulmonary coupling is analyzed by multi-variable time sequence analysis technology, multiple variables such as cardiac cycle, respiration and blood pressure are analyzed by a frequency domain and information analysis method, analysis deficiency of single variable is effectively remedied, so that a complicated regulation mechanism between cardiopulmonary systems is more accurately and comprehensively quantified, the coupling strength of cardiopulmonary interaction is quantified, and the direction of cardiopulmonary interaction can be judged.

The invention discloses a multi-sensor heart-lung coupling sleep quality detection system and a detection method thereof. According to the invention, an ultrathin flexible strain sensor and an electrocardiogram sensor are adopted for being easily attached to the body surface of a sleeper and will not generate any discomfort or constraint feel; in a measuring process, the existence of the sensors almost cannot be sensed; the influence on the sleeper is extremely reduced and the state of the sleeper in a sleeping state can be truly reflected. The ultrathin flexible strain sensor is used for measuring the breathing and electrocardiogram signals, and then a heart-lung coupling algorithm is used for comprehensively analyzing the breathing and electrocardiogram signals, so that a quantitative index parameter of the sleeping quality of the patient and the apnea characteristic moment of obstructive sleep apnea syndrome can be acquired; the signals are wirelessly transmitted to a mobile terminal; the volume of the whole system is small; the system can be conveniently used in different sleeping occasions; the multi-sensor heart-lung coupling sleep quality detection system is simple and practicable, high in accuracy, convenient in use and comfortable in feel.

The embodiment of the invention provides a non-contact sleep assessment method and device based on CPC. The method comprises the steps of generating micromotion data which is generated according to amicrovibration signal of body organs, conducting multi-resolution wavelet transform processing on the micromotion data to obtain a heart rate representation signal and a respiratory rate representation signal, extracting a corresponding heart rate sequence and a respiratory rate sequence from the heart rate representation signal and the respiratory rate representation signal respectively, and generating a corresponding coherence spectrum, a cross-power spectrum and a cardiopulmonary coupling spectrogram for sleep assessment according to the heart rate sequence and the respiratory rate sequence. Multiple types of sign parameters do not need to be collected, the data collection dimensionality and difficulty are lowered, the equipment cost is also lowered, and popularization is facilitated. Meanwhile, since the micromotion data of the organs is adopted to conduct multi-resolution wavelet transform processing, the heart rate representation signal and the respiratory rate representation signal are separated out; therefore, a micromotion sensing device does not need to be in contact with the human body surface, and the influence on a testee is avoided.

The invention discloses a sleep staging method based on BCG and blood oxygen saturation rate. The sleep staging method comprises the following steps of collecting BCG data and blood oxygen saturationdata of the whole night; extracting a JJ interval from BCG signals to obtain a heart rate signal and heart rate variability; processing the BCG signal to obtain a respiratory rate signal, and calculating a cardiopulmonary coupling power spectrum; and establishing a state set S = {S1, S2, S3, S4} of the sleep process. According to the sleep staging method based on the BCG and the blood oxygen saturation rate, the accuracy degree of sleep staging of non-contact information collection can be improved, the characteristics of a sober state are extracted in a cardiopulmonary coupling power spectrummode, sleep staging training is optimized by a machine learning clustering analysis method, the accuracy degree of sleep staging is improved, and a user can be helped to obtain an accurate examinationresult of the sleep state in a non-inductive environment.

A cardiopulmonary coupling feedback method comprises the steps that firstly, the cardiopulmonary coupling strength of an examinee is obtained by carrying out coupling analysis on cardiovascular signals and respiratory signals of the examinee; secondly, movement indicating signals are output to the examinee according to the cardiopulmonary coupling strength of the examinee so as to improve the cardiopulmonary coupling strength of the examinee. By means of a cardiopulmonary coupling feedback device and the cardiopulmonary coupling feedback method, a user can carry out specific movement under the guidance of the cardiopulmonary coupling strength to improve the cardiopulmonary coupling strength constantly. The synergic efficiency of the cardiopulmonary system is improved.

The invention discloses a sleep staging method, system and device based on a ballistocardiogram signal and a medium. The method comprises the following steps: collecting a first ballistocardiogram signal of a human body; acquiring a first heartbeat signal and a first respiration signal according to the first heart impact signal; acquiring a first heart rate variability and a first cardiopulmonary coupling power spectrogram according to the first heartbeat signal and the first respiration signal; inputting the first heart rate variability and the first cardiopulmonary coupling power spectrogram into a sleep staging model for feature extraction, performing sleep staging according to extracted features, and outputting a staging result; wherein the sleep staging model is obtained by training by adopting electrocardiosignals and then adjusting by adopting ballistocardiogram signals on the basis of transfer learning. The sleep staging is realized through the ballistocardiogram signal, and since the ballistocardiogram signal can be acquired in a non-invasive manner, the comfort of sleep monitoring is improved. The method can be widely applied to the field of sleep monitoring.

The invention provides a sleep staging method based on BLSTM and a sleep staging device on the BLSTM. The BLSTM comprises two training layers and one output layer, wherein each training layer comprises 16 units, the output layer comprises 4 units, and the four units correspond to an awakening stage, a light sleep stage, a deep sleep stage and a rapid eye movement stage of sleep respectively; and input characteristics of the BLSTM comprise a characteristic extracted from an RR interphase signal, a characteristic extracted from a breathing signal and a characteristic extracted from cardiopulmonary coupling. The method and device acquire cardiopulmonary signals of a testee using a low-cost wearable multi-sensor system, conduct four-classification sleep staging by a bidirectional recursive neural network model which has an LSTM function and realize accurate staging of sleep.

The invention relates to a sleep state staging method and a device based on cardiopulmonary coupling and a computer readable storage medium, and relates to the field of sleep monitoring. The method comprises the steps of obtaining an initial heart rate signal and an initial respiration signal based on a piezoelectric induction signal; respectively filtering and smoothing the initial heart rate signal and the initial breathing signal to obtain a heart rate waveform signal and a breathing waveform signal; performing waveform time domain conversion on the heart rate waveform signal and the breathing waveform signal to obtain a time domain heart rate signal and a time domain breathing signal; acquiring a time-domain heart rate signal and a time-domain breathing signal of the detection period, performing Fourier transform, and calculating to obtain a frequency-domain heart rate signal and a frequency-domain breathing signal; acquiring heart rate features from the frequency-domain heart rate signal, and acquiring breathing features from the frequency-domain breathing signal; based on a cardiopulmonary coupling algorithm, calculating cardiopulmonary coupling features through the heart rate features and the breathing features; and outputting judged sleep state stages according to the cardiopulmonary coupling features. The method and the device provided by the invention have the effect of monitoring the individual sleep state by stages.

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 relates to a sleep quality monitoring system and method based on a magnetocardiogram. The monitoring method comprises the steps of S10, collecting magnetocardiogram signals and blood oxygen signals of the sleep time of a target user by means of the monitoring system; S20, performing preprocessing and noise reduction processing on the magnetocardiogram signals, screening available magnetocardiogram signal time periods, and obtaining a middle magnetocardiogram signal; S30, extracting the middle magnetocardiogram signal by adopting a QRS wave detection method and a cardiopulmonary coupling algorithm to obtain a characteristic signal for reflecting the sleep quality of the target user; S40, conducting preprocessing and characteristic value extraction on the blood oxygen signals, and adopting a pre-trained machine learning model for screening a time period capable of reflecting a breathing event of the target user; and S50, based on the characteristic value and the characteristic signal of the blood oxygen signals in the screened time period, obtaining a sleep quality evaluation result of the target user. The method can be used for monitoring the breathing event in time sequence and improving the detection accuracy and credibility.

The invention provides a cardiopulmonary coupling auxiliary respirator, and belongs to the field of medical equipment. The cardiopulmonary coupling auxiliary respirator comprises a base, a storage box, an ultraviolet disinfection cabinet, cardiopulmonary coupling equipment, a ventilator table, a ventilator, a breathing mask and a filter tube. According to the cardiopulmonary coupling auxiliary respirator, the auxiliary ventilator and the cardiopulmonary coupling equipment are combined together for use through a sliding rail structure, the occupied space is reduced, the operation steps are simplified, and the working efficiency is improved; activated carbon and a UV lamp are arranged in the filter tube, so that air is subjected to dual efficient purification, and the breathing quality of auser is improved; and meanwhile, due to the arrangement of a special mask disinfection chamber and other disinfection chambers, cross infection is prevented from happening, and user health is prevented from being harmed.

The invention discloses a cardiopulmonary coupling analysis method based on a single-channel ECG signal. The cardiopulmonary coupling analysis method specifically comprises the steps that an RR interval sequence and an EDR signal are extracted from a body surface single-lead ECG signal; interpolating the RR interval sequence and the EDR sequence to the same sampling rate by using cubic spline interpolation; calculating an autocorrelation function and a cross-correlation function of the RR interval sequence and the EDR sequence; fourier transform is carried out to respectively obtain self-correlation power and cross-correlation power; calculating a cardiopulmonary coupling result through the cross-correlation power to obtain a cardiopulmonary coupling map; and analyzing the sleep quality according to the cardiopulmonary coupling atlas. The cardiopulmonary coupling result is calculated through the cross-correlation power, then the cardiopulmonary coupling map is obtained to analyze the sleep quality, the sleep quality analysis accuracy is improved, the method can be used for home detection to reflect the real sleep condition, accurate judgment on the sleep quality of a user is facilitated, and making of a personalized conditioning scheme is facilitated.

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 discloses a fatigue sleep analysis method. The method comprises the following steps of step 1, collecting a BCG signal of a user; step 2, performing first filtering on the original BCG signal; step 3, carrying out second filtering on the original BCG signal; step 4, carrying out abnormal value elimination on the characteristic peaks; step 4, adopting a Lomb-Scargle algorithm for conducting power spectrum calculation on the respiration signals and the heart rate variability signals; step 5, conducting cardiopulmonary coupling analysis on the respiration signals and the heart rate variability signals; and step 6, classifying the cardiopulmonary coupling strength by adopting a classifier model in machine learning so as to obtain an analysis result of fatigue and sleep states. The method can process non-equidistant sampling signals, is not sensitive to the interference of abnormal points, and can obtain higher frequency precision. According to the algorithm, the detection precision of the algorithm is improved, the complexity of the algorithm is reduced, and the application range is wide.

In accordance with one aspect, a system for evaluating a health condition includes a storage containing cardiopulmonary coupling (CPC) data and heart rate data for a person for a sleep time period, one or more processors, and a memory storing instructions. When the instructions are executed by the processor(s), they cause the system to access the CPC data and the heart rate data from the storage,identify one or more time periods in the sleep time period categorized as high frequency coupling (HFC) state based on the CPC data, evaluate one or more characteristics of a portion of the heart ratedata corresponding to the one or more time periods, and evaluate health condition of the person based on the one or more characteristics of the portion of the heart rate data corresponding the one ormore time periods categorized as HFC state.

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 invention discloses a non-contact type cardiopulmonary coupling evaluation method, which comprises the following steps of: correctly detecting a pulse signal and a respiration signal of a shot person by shooting a video image of the upper part of a human body in a quiet state, carrying out cardiopulmonary coupling time domain and frequency domain analysis, and evaluating and measuring the cardiopulmonary coupling strength. The method comprises the following steps: shooting an upper body video of a subject through a camera, performing Gaussian pyramid decomposition and Laplacian pyramid decomposition on the video, and amplifying pulse-related color change and upper body motion information caused by respiration in the video; a head and face region of interest containing pulse information and a thoracic and abdominal region of interest containing respiratory movement are automatically extracted, brightness information in the regions of interest is calculated, so that pulse signals and respiratory signals are extracted, time domain and frequency domain analysis of cardiopulmonary coupling is carried out through synchronization processing of the pulse signals and the respiratory signals, and the accuracy of heart and lung analysis is improved. And carrying out cardiopulmonary coupling evaluation according to the coupling strength.

The embodiment of the invention provides a non-contact sleep assessment method and device based on CPC. The method comprises the steps of generating micromotion data which is generated according to amicrovibration signal of body organs, conducting multi-resolution wavelet transform processing on the micromotion data to obtain a heart rate representation signal and a respiratory rate representation signal, extracting a corresponding heart rate sequence and a respiratory rate sequence from the heart rate representation signal and the respiratory rate representation signal respectively, and generating a corresponding coherence spectrum, a cross-power spectrum and a cardiopulmonary coupling spectrogram for sleep assessment according to the heart rate sequence and the respiratory rate sequence. Multiple types of sign parameters do not need to be collected, the data collection dimensionality and difficulty are lowered, the equipment cost is also lowered, and popularization is facilitated. Meanwhile, since the micromotion data of the organs is adopted to conduct multi-resolution wavelet transform processing, the heart rate representation signal and the respiratory rate representation signal are separated out; therefore, a micromotion sensing device does not need to be in contact with the human body surface, and the influence on a testee is avoided.

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 invention relates to the technical field of cardiopulmonary coupling relationship analysis, and discloses an information gain-based cardiopulmonary coupling relationship analysis method, which comprises the following steps: acquiring an electrocardiosignal and a respiratory signal of a human body to form an electrocardiosignal sequence and a respiratory signal sequence, and respectively preprocessing the acquired electrocardiosignal sequence and respiratory signal sequence; filtering the pre-processed electrocardiosignal sequence and the pre-processed respiration signal sequence through a variable-step-size self-adaptive filtering algorithm; performing empirical mode decomposition on the filtered electrocardiosignal sequence and respiratory signal sequence; and carrying out information gain calculation on the decomposed electrocardiosignal sequence and respiratory signal sequence. According to the method, empirical mode decomposition is conducted on an electrocardiosignal sequence and a respiratory signal sequence, information gain calculation is conducted on the decomposed signal sequences, the smaller the information gain is, the stronger the cardiopulmonary coupling relation is, and therefore cardiopulmonary coupling relation analysis is achieved.