1084results about "Stethoscope" patented technology

The invention relates to the technical field of biomedical signal processing and intelligent health monitoring, and particularly discloses an intelligent heart rate monitoring method and system based on heart sound and electrocardiosignals, heart sound signals and electrocardiosignals of a user are collected in real time, quality evaluation is conducted on the collected signals through signal quality indexes, and accurate signals are extracted; extracting time domain and frequency domain features of the first heart sound and the second heart sound of the heart sound signal, fusing multiple features by adopting wavelet transform and a main frequency analysis algorithm, and calculating a heart sound abnormal coefficient to evaluate the abnormal degree of the heart sound signal; the vibration amplitude and frequency characteristics of P waves in the electrocardiosignals are extracted, and an electrocardiograph abnormal coefficient is calculated through a vibration amplitude abnormal coefficient and a vibration frequency fluctuation coefficient and used for evaluating the stability of the electrocardiosignals; based on the heart sound abnormal coefficient and the electrocardiogram abnormal coefficient, a decision tree model is constructed, a heart rate abnormal index is dynamically calculated, dynamic monitoring of the heart rate is achieved, a dynamic early warning mechanism is conducted, and heart rate abnormal information is output.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

The invention provides an intelligent monitoring system and method for swallowing rehabilitation, and the method comprises the steps: determining the mistaken aspiration risk feature of a pressure peak value in the swallowing rehabilitation through the high-frequency energy sudden change amount of cough sound and the delay feature of a food mass through the pharynx, and when the mistaken aspiration risk feature is greater than a risk threshold value in the swallowing rehabilitation, stopping the swallowing rehabilitation. According to biomechanical information in swallowing rehabilitation, coordination scores of all rehabilitation stages in the swallowing rehabilitation process are determined, and then function recovery indexes of the swallowing function in the pharyngeal period in the swallowing rehabilitation process are determined according to all the coordination scores and mistaken aspiration risk characteristics. The swallowing efficiency index in swallowing rehabilitation is extracted, then the rehabilitation deviation characteristic of the pharynx is determined through the swallowing efficiency index and the function recovery index, and personalized adjustment is conducted on the swallowing rehabilitation scheme after sound-light alarm based on the rehabilitation deviation characteristic. Based on the scheme, personalized coordination of the rehabilitation scheme in swallowing rehabilitation can be realized, so that the recovery efficiency of the swallowing function in the pharyngeal period can be improved.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

A portable physiological measurement device (100) is proposed that can be grasped by a manipulator, comprising: - a housing (102) of elongated shape along an extension direction (X) and comprising, along the extension direction, a first end (102L) and a second end (102R), - a first physiological end sensor (106L) positioned at the first end (102L), - a second physiological end sensor (106R) positioned at the second end (102R), - a physiological finger sensor (110L, 110R, 114) arranged on the housing (102) near the first end (102L) or the second end (102R).

The invention relates to the technical field of stoma nursing devices, and discloses an AI visual stoma leakage early warning system, which comprises a plurality of passive piezoelectric film sensors embedded in a stoma chassis and used for capturing acoustic signals generated by adhesion interface structure instability; the edge calculation unit periodically collects an electric signal sequence and calculates an information entropy value, and through comparison with a self-adaptive resting entropy baseline, the structural instability risk is judged and early warning is generated when the entropy value continuously and abnormally rises. The risk orientation is accurately identified in combination with a multi-sensor cooperative positioning technology, body movement interference is avoided by using a dynamic compensation mechanism, and the early warning accuracy and clinical practicability are improved.

The invention provides a result judgment and use method of a portable borborygmus monitor, and relates to the technical field of medical monitoring equipment.The method comprises the steps that abdominal borborygmus is collected through a piezoelectric ceramic sensor array in multi-channel triangular distribution, and breathing and body movement interference is eliminated in combination with a dynamic noise filtering algorithm; extracting borborygmus outbreak duration, interval period and time-frequency energy indexes, and constructing an activeness and rhythm disorder two-parameter evaluation system; dynamically adapting a reference threshold value by adopting an age piecewise function, and introducing a feeding time compensation factor to optimize an individualized criterion; the intestinal obstruction risk is judged through a time sequence matching algorithm and a multi-parameter classification model dual-verification mechanism; a three-dimensional dynamic map is generated based on a sound source localization algorithm and spectral analysis, and data integrity is guaranteed in combination with a priority transmission protocol. According to the method, spatial-temporal feature analysis and an intelligent decision model are fused, sensor anti-interference, individual adaptability and pathological early warning precision are considered, and the core pain points of high misjudgment rate and poor clinical compatibility of traditional equipment are solved.

The invention discloses a multi-mode-based flexible wearable heart function monitoring method, system and device, and relates to the technical field of health monitoring, and the method comprises the steps: outputting a multi-mode signal through a flexible patch sensing module when the flexible patch sensing module is arranged in a detection area; wherein the flexible patch sensing module comprises an electrocardio sensor, a pulse wave sensor, a heart sound sensor, a strain sensor and a microfluidic biosensor. According to the invention, high-precision synchronous signal acquisition is realized through flexible patch integrated multi-modal sensing, heart multi-source signal features are adaptively extracted in combination with a multi-scale convolutional neural model, and timing sequence relevance is captured by using a bidirectional long-short-term memory network, so that the accuracy and reliability of heart disease diagnosis are remarkably improved, and the accuracy and reliability of heart disease diagnosis are improved. Meanwhile, non-invasive and continuous monitoring and early pathological screening are supported.

The invention discloses a heart sound and electrocardio acquisition and analysis system, which relates to the technical field of biomedical engineering, and comprises a time-frequency analysis module for acquiring a heart sound and electrocardio original data set through an electrocardio and heart sound lead suction ball and an electrocardio and heart sound simulation complete machine and transmitting the data set to an upper computer to execute wavelet decomposition and short-time Fourier transform, the system comprises a heart sound time-frequency spectrum matrix and an electrocardio time-frequency energy matrix output module, a chaotic feature extraction module, the heart sound time-frequency spectrum matrix and the electrocardio time-frequency energy matrix are subjected to phase-space reconstruction, a high-dimensional dynamic track is formed, an improved wolf algorithm is applied to conduct dynamic index calculation on the high-dimensional dynamic track, and a dynamic parameter set is obtained. According to the method, through the improved wolf algorithm and the constructed heart sound space propagation model, the multi-modal fusion capability and analysis discrimination between the heart sound and the electrocardiosignal are improved, and the intelligent level of heart sound and electrocardiosignal collection and analysis is also improved.

The invention discloses a borborygmus detection system and method based on multi-sensor fusion and deep learning, and the method comprises the following steps: S1, constructing a multi-mode sensor array for collecting abdominal acoustics, vibration and motion signals; s2, adaptive filtering, noise suppression and data synchronization preprocessing are carried out on the collected signals; s3, performing multi-sensor data fusion and voting on the filtered signal; s4, constructing a deep learning classification model; according to the method, acoustics, vibration and motion signals are synchronously collected based on a multi-mode sensor, abnormal data are eliminated through a voting algorithm, a fusion result is optimized in combination with Kalman filtering, filter parameters can be dynamically adjusted according to priori knowledge of borborygmus, key frequency bands are reserved, and characteristics of different borborygmus types are extracted in a targeted mode. The classification accuracy is improved by more than 15%, high-precision classification is realized, and F1-score of normal / hyperfunction / weakening sound on a test set reaches 92.5%.

A snore recognition method and apparatus, a device, and a storage medium. The method comprises: acquiring an audio signal within a preset time period (S101); plotting an envelope line on the basis of the audio signal, and acquiring the number of envelopes in the envelope line (S102), wherein each envelope comprises a section of ascending curve and a section of descending curve; acquiring associated data of a starting point, a midpoint and an end point of each envelope (S103), wherein the midpoint is a point with a maximum sound loudness value, and the associated data comprises sound loudness; and performing snore judgment on the audio signal within the preset time period on the basis of the associated data of the starting point, the midpoint and the end point of each envelope; and if the sound loudness of the starting point, the sound loudness of the midpoint and the sound loudness of the end point are all different, and the sound loudness of the midpoint is greater than or equal to a second preset loudness, judging that the audio signal within the preset time period has a snore (S104). The snore is recognized on the basis of the sound loudness of the audio signal, and a recognition algorithm is simple.

The present description relates to methods and systems for a medical dictation. In one example, a stethoscope includes a first microphone positioned to capture physiological sounds of a patient, a second microphone positioned to capture ambient sounds, one or more processors, and memory storing instructions executable by the one or more processors to: during a stethoscope mode, obtain a first signal from the first microphone and a second signal from the second microphone, process the first signal to capture a physiological sound signal, including performing noise cancellation on the first signal based on the second signal, and transmit the physiological sound signal to an external computing device and / or a speaker of the stethoscope; and during a dictation mode, obtain the second signal from the second microphone, process the second signal to capture a voice signal, and transmit the voice signal to the external computing device.

The embodiment of the invention provides a real-time pulmonary ventilation impedance monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining real-time multi-modal data from a plurality of sensors, the multi-modal data comprising a sound signal, an airflow signal and a pressure signal; performing fusion processing on the multi-modal data to generate comprehensive pulmonary ventilation impedance data; based on the comprehensive pulmonary ventilation impedance data, analyzing data distribution characteristics to detect anomalies; and when abnormal data distribution characteristics are detected, a fault early warning system is triggered to solve burst signal distortion. Through the scheme of the embodiment of the invention, the problem of how to carry out fault early warning on the monitoring system according to the abnormal data distribution characteristics so as to solve the problem of burst signal distortion can be solved.

The embodiment of the invention provides a continuous breath sound monitoring application system, which is oriented to double versions of a medical professional version and a civil public version, can continuously collect and analyze breath sound, adopts a multi-level label system to carry out classified labeling, and combines expert labeling and machine learning to improve the recognition accuracy. And meanwhile, a perfect database architecture and a local / cloud mixed storage strategy are provided to ensure reliable management of data, and data security compliance is ensured through encryption and access control. The system is also integrated with an interactive teaching module to meet the requirements of professional training and popular science popularization, and is equipped with a multi-parameter linkage grading alarm mechanism to provide real-time early warning. In addition, the system has good expandability and can be integrated with a hospital information system (HIS) and a remote medical platform through standard interfaces.

The invention relates to the technical field of man-machine collaboration and information processing, discloses an AI processing-based motion capture recognition pre-judgment system, and aims to solve the problems of difficulty in physiological signal monitoring and performance drifting of a myoelectricity intention recognition model in a high-noise environment, and the AI processing-based motion capture recognition pre-judgment system comprises a processing module, a visual capture module, a myoelectricity acquisition module, a bioacoustics acquisition module and an information prompting module, according to the method, myoelectricity and visual information decoding operation intentions are fused, a self-adaptive filter is guided through the intentions, accurate noise suppression is carried out on biological acoustic signals of a target object, and therefore the physiological state risk of the target object is accurately evaluated, meanwhile, an online self-adaptive calibration mechanism is introduced, the actual action of visual recognition serves as a supervision true value, and the accuracy of the physiological state risk of the target object is improved. And the myoelectricity model is continuously calibrated, so that the accuracy of long-term identification is ensured. According to the method, the decoding intention and the physiological risk level are integrated, the composite cooperation instruction is intelligently generated and fed back to the operator, the situation awareness ability of the operator is enhanced, and the safety and efficiency of man-machine cooperation are improved.

The invention relates to the technical field of medical equipment, in particular to a cardiopulmonary sound and electrocardiogram rapid screening system suitable for emergency treatment scenes. The system comprises a heart and lung sound acquisition unit, an electrocardiogram acquisition unit and an intelligent analysis unit. And the intelligent analysis unit realizes signal synchronization by establishing a cross-modal time sequence alignment model, adaptively matches the physiological state by adopting a dynamic time window adjustment mechanism, and introduces multiple rounds of verification processes to ensure the result reliability. The system can automatically complete synchronous collection, feature extraction and fusion analysis of the heart and lung sound and the electrocardiosignals, the diagnosis accuracy is remarkably improved while the screening speed is guaranteed, and reliable technical support is provided for rapid screening of heart and lung diseases in the emergency department.

The invention discloses a child mouth breathing monitoring method, device and equipment and a medium, and the method comprises the steps that a non-contact sensor group is used for synchronously collecting multi-mode physiological data of a target child in the sleep period, and the multi-mode physiological data at least comprises a face thermal imaging video stream, a thoracic and abdominal micro-motion signal and an environment audio signal; the multi-modal physiological data is processed, multi-modal features related to respiration are extracted respectively, and the multi-modal features comprise thermodynamic change features of mouth and nose areas extracted based on a face thermal imaging video stream, respiratory effort waveforms extracted based on thoracic and abdominal micro-motion signals, and the respiratory effort waveforms extracted based on the thoracic and abdominal micro-motion signals; extracting a sound source spatial position and a spectrum feature of breathing sound based on the environment audio signal; inputting the multi-modal features into a pre-trained mouth breathing recognition model, and outputting a judgment result of the functional mouth breathing event of the target child within a specific time period; and generating a mouth breathing monitoring report of the target child based on the judgment result.

The invention discloses a digital stethoscope system based on a piezoelectric film, and relates to the technical field of medical electronics and biomedical engineering. An existing digital stethoscope based on a piezoelectric film has the challenges of signal noise and interference, limitation of a signal processing algorithm, nonlinear response and time migration, insufficient diagnosis accuracy and automation and the like. A piezoelectric film and microphone dual audio acquisition mode is fused, various physiological signals such as low-frequency vibration and high-quality heart and lung sound are captured, and background noise is removed through a self-adaptive filter and a frequency spectrum subtraction technology; an MFCC feature extraction and dynamic time warping algorithm is utilized to carry out fine feature analysis and time axis alignment on the preprocessed audio signal, and high-precision matching with a storage template is realized; different physiological states are accurately distinguished through intelligent feature processing, real-time and visual diagnosis feedback is achieved through wireless transmission and state indication, and the reliability and efficiency of medical detection are improved.

The invention provides a mitral valve regurgitation severity assessment method based on PCG signals, which comprises the following steps: firstly, synchronously acquiring signals from a heart sound area and an environmental background by adopting a dual-channel audio acquisition technology, eliminating environmental noise and operation interference (such as clothes friction and non-uniform pressing force of a stethoscope) through a self-adaptive noise elimination algorithm, and calculating the severity of the mitral valve regurgitation severity; and carrying out segmentation processing on continuous heart sound signals of the de-noised heart sound by adopting a dynamic time window segmentation strategy to generate short-time heart sound fragments, evaluating the quality of the short-time heart sound fragments in real time by using a model based on a lightweight convolutional neural network, and filtering low-quality fragments. Then, nonlinear features are directly extracted from the short-time heart sound fragments through a deep neural network model based on a clique block, after each effective fragment is subjected to four classifications, classification results of all the fragments are integrated through a majority voting mechanism, and final mitral valve regurgitation severity judgment is generated. The classification robustness is effectively improved through multi-fragment information fusion, and the random error of single-fragment analysis is reduced.

The invention discloses a heart sound, electrocardio and echocardiogram multi-modal data phase synchronization method, which belongs to the technical field of medical image processing, and comprises the following steps: A1, obtaining an original echocardiogram video and independently recorded heart sound audio data information; a2, preprocessing the acquired data and performing sample automatic construction; a3, a double-path neural network architecture is constructed, electrocardiogram waveform extraction is carried out, and a digital electrocardiogram signal sequence is obtained; a4, periodic feature points of the heart sound and the electrocardio are recognized based on the digital electrocardio signals; and A5, aligning the asynchronous data based on linear phase resampling. According to the method, the video frames, the electric signals and the heart sound signals under different sampling rates are forcibly mapped into the unified phase coordinate system through a linear interpolation algorithm, absolute synchronization of the video frames, the electric signals and the heart sound signals in cardiac pulsation physiological logic is finally achieved, and the influence of individual heart rate differences on data alignment is eliminated.

An apparatus can be used for detecting acoustic signals of a vascular system. The apparatus can comprise at least one acoustic sensor. Each acoustic sensor can comprise a piezoelectric layer defining a first side and a second side, and a first annular electrode disposed on the first side of the piezoelectric layer. The first annular electrode can define a hole therethrough. A second annular electrode can be disposed on the second side of the piezoelectric layer disposed against the second. A polymer engagement layer can be positioned against the first side of the piezoelectric layer and disposed at least partially within the hole of the first annular electrode. Methods of data processing for data collected via the apparatus are also disclosed herein.

The invention relates to the technical field of audio signal processing and biomedical signal analysis, and still has a further optimized space for the recognition of anti-noise requirements, signal individual differences and complex pathological modes in a noise environment. The invention provides a double-path CNN heart sound classification method based on time-frequency and double-spectrum fusion features, and the method comprises the steps: carrying out the preprocessing of an original heart sound signal of a data set which is classified into a normal heart sound and an abnormal heart sound, and obtaining a to-be-recognized heart sound signal; based on dynamic continuous wavelet transform, adaptively selecting parameters to extract time-frequency characteristics, introducing bispectrum analysis, capturing nonlinear characteristics, generating a dual-channel characteristic pattern, and efficiently storing the dual-channel characteristic pattern in an HDF5 format; and based on a designed double-path convolutional neural network structure, respectively processing the extracted time-frequency and double-spectrum features, performing classification after fusion, and training a model in combination with category weighted loss and an optimization strategy to obtain a heart sound classification result. The heart sound recognition accuracy can be improved.

The invention discloses a breathing and electrocardio information integrated recording and displaying method and system based on overall spatial-temporal characteristics. The method comprises the steps that a high-precision sensor synchronously collects electrocardiogram, phonocardiogram, arterial pulse waveform, venous pulse waveform and breathing wave signals and preprocesses the signals based on different signal characteristics; carrying out multi-cycle electrocardiogram merging by utilizing an R-wave detection algorithm and introducing a variational mode decomposition algorithm; carrying out feature extraction from the multi-mode signal, and carrying out respiratory wave and electrocardiowave conjoint analysis; carrying out feature fusion through a spatial-temporal feature fusion network of a multi-branch attention mechanism, and generating and displaying a combined electrocardiograph distribution diagram; carrying out anomaly detection through an anomaly detection model based on a Transform architecture; synchronously displaying in an integrated display interface; according to the system and the method, efficient acquisition, analysis and visualization of the multi-modal physiological signals are realized by integrating synchronous acquisition, intelligent interpretation and integrated display of various physiological signals, and the efficiency and the accuracy of clinical diagnosis are favorably improved.

The invention relates to a heart health state detection method and device and electronic equipment, and the method comprises the steps: determining a plurality of first feature sets and a plurality of second feature sets in a short-time feature set based on a to-be-detected heart sound signal, each first feature set being determined based on the signal amplitude of the heart sound signal in a corresponding window at each sampling point, and each second feature set being determined based on the signal amplitude of the heart sound signal in the corresponding window at each sampling point; each second feature set is determined based on the duration of a first heart sound interval, a systolic period, a second heart sound interval and a diastolic period included in each cardiac cycle in the heart sound signal in the corresponding window; based on the heart sound signal to be detected, a time domain feature set, a frequency domain feature set and a Poincare map related parameter set in the long-time feature set are determined, the time domain feature set is determined based on the duration of each cardiac cycle, the frequency domain feature set is determined based on signal power, and the Poincare map related parameter set is determined based on a Poincare map generated based on the duration of each cardiac cycle; and inputting the short-time feature set and the long-time feature set into a set heart health state detection model to obtain a heart health state result.

The invention particularly relates to a respiratory disease diagnosis device and method, and the device comprises a respiratory sound signal collection unit which is used for obtaining a current respiratory sound signal; the breathing sound signal preprocessing unit is used for performing segmentation processing and denoising processing on the current breathing sound signal to obtain a preprocessed breathing sound signal; the feature extraction unit is used for extracting multi-modal features of the preprocessed breath sound signals; and the detection unit is used for inputting the multi-modal features into a preset deep learning network model to obtain a respiratory system disease detection result. Therefore, through multi-modal feature extraction, dynamic fusion and an efficient classification strategy, the problems of relatively low feature identification degree, relatively low disease classification accuracy and the like in the process of researching disease diagnosis based on the breath sound signals in related technologies are solved, and the detection performance of chronic respiratory system diseases based on the breath sound signals is remarkably improved.

The invention discloses a portable detection system for early diagnosis of orthopaedic joint diseases, and relates to the technical field of orthopaedic joint disease diagnosis, which comprises the following steps: acquiring acoustic and motion feature vectors of joint activities, calculating a quality score based on an acoustic signal-to-noise ratio and a motion goodness of fit, performing weighted splicing on features by taking the quality score as a weight after normalization, generating a fusion feature vector; thirdly, reconstructing features by using a reverse decoding model, comparing the same degree with the original features, weighting to obtain fusion same degree, and iteratively adjusting the weight to optimize the fusion effect; and performing clustering analysis on the candidate feature vectors, replacing the candidate feature vectors if the candidate feature vectors deviate from a clustering center, and finally inputting a health model to output a diagnosis report. Through dynamic weighting of quality scores, high-fidelity features are processed preferentially, the information density and discrimination capability are improved, and low-quality data interference is reduced; and self-checking and dynamic correction are realized through reconstruction and similarity comparison, so that the fidelity and consistency of the characteristics are ensured, and the robustness of the system is enhanced.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

A system includes one or more sensors to detect activities of a mobile object; and a processor coupled to the sensor and the wireless transceiver to classify sequences of motions into groups of similar postures each represented by a model and to apply the models to identify an activity of the object.

Presented herein are techniques for detecting the heartbeat of the recipient of an implantable medical device. The implantable medical device includes sensors for capturing audio signals and vibrations signals from the body of the recipient. A heartbeat detection module filters at least the vibration signals to generate a heartbeat signal representing the heartbeat of the recipient.

The invention belongs to the technical field of signal analysis, and discloses an electrocardio and heart sound signal cross-modal bidirectional coupling coronary heart disease detection method and system, and the method comprises the steps: obtaining an electrocardio signal and a heart sound signal of a testee, carrying out the preprocessing, and extracting the time-frequency domain features of the obtained signals; the signal-to-noise ratio of the electrocardiosignal and the heart sound signal is calculated, the signal-to-noise ratio sensing weight is calculated according to the signal-to-noise ratio, cosine similarity calculation is carried out based on the signal-to-noise ratio sensing weight, and the instantaneous form similarity of the time domain features of the electrocardiosignal and the heart sound signal is quantified; calculating mutual information of the frequency domain characteristics of the electrocardiosignal and the heart sound signal, and generating a fusion weight according to the instantaneous form similarity of the mutual information and the time domain characteristics; and inputting the fusion weight into a classifier for coronary heart disease detection. By constructing the bidirectional cross-modal coupling model, a dynamic interaction mechanism between electrical activity and mechanical motion is disclosed, the limitation of traditional single-modal analysis is overcome, and the sensitivity and specificity of early diagnosis of the coronary heart disease are remarkably improved.