266 results about "Cardiac sounds" 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 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 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.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

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 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 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.

The invention provides a heart sound anomaly detection method and device, electronic equipment and a storage medium. An original heart sound signal is acquired and preprocessed into a standard heart sound signal, a plurality of cardiac cycles are divided to form a periodic heart sound signal, and corresponding Mel filter bank coefficient features and Mel frequency cepstral coefficient features are extracted; fusing the Mel filter bank coefficient features and the Mel frequency cepstrum coefficient features, inputting the fused Mel filter bank coefficient features and Mel frequency cepstrum coefficient features into an encoder in a pre-trained MobileNetV2 network, capturing a long-distance dependency relationship in the periodic heart sound signals by using a self-attention mechanism, and outputting a potential space representation vector; and inputting the potential space representation vector into a classifier of the MobileNetV2 network, and outputting a prediction result through a Softmax function. According to the method, the model complexity can be greatly reduced while the model precision is guaranteed, effective features can be extracted while low calculation overhead is kept, and the recognition capability and generalization performance of the model on abnormal heart sounds can be enhanced.

The invention discloses a heart sound data processing method and system based on a time-frequency characteristic graph, and relates to the technical field of electric digital data processing. The heart sound data processing method based on the time-frequency characteristic graph comprises the following steps: steadily dividing heart sound, optimizing and identifying, and identifying and verifying noise. According to the method, feature stationary difference division is carried out on the time-frequency feature map obtained based on the heart sound signals to obtain the heart sound stationary difference feature result, then heart sound optimization processing is carried out in combination with the heart sound stationary difference feature result, the noise level recognition result is obtained, finally, the noise level recognition result is verified, and if verification succeeds, the noise level recognition result is recognized. If yes, feeding back the noise level recognition result, otherwise, updating the noise level recognition result, so that the effect of more accurately recognizing the noise of the heart sound signal is achieved, and the problem that the stability difference of the heart sound signal is not fully considered in the heart sound signal noise recognition process based on the time-frequency feature map in the prior art is solved.

The invention discloses an intelligent heart sound recognition method, system and device and a medium, and the method comprises the steps: obtaining a heart sound signal, and carrying out the preprocessing of the heart sound signal; filtering noise of the heart sound signal; extracting local features and time-frequency features of the heart sound signals; inputting the heart sound signal features into a heart sound recognition model, wherein the heart sound recognition model comprises a first neural network, a second neural network and a Transform model; the first neural network is used for identifying the time-frequency features to obtain a first identification result; the second neural network is used for processing the local features to obtain a second recognition result; the Transform model is used for processing the heart sound signal to obtain a third recognition result; and fusing the first recognition result, the second recognition result and the third recognition result to obtain a heart sound recognition result. According to the method, the heart sound signals are efficiently and accurately recognized based on the heart sound recognition model of machine learning.

The invention provides an MEMS heart sound and electrocardio detection system, method and equipment, and relates to the technical field of medical instruments, the MEMS heart sound and electrocardio detection system comprises a bionic cilium heart sound sensor, an electrocardio acquisition circuit and a signal fusion model; the bionic cilia heart sound sensor comprises hollow bullet type bionic cilia and a cantilever beam microstructure; the hollow bullet type bionic cilia and the cantilever beam microstructure are mechanically coupled, and the cantilever beam microstructure comprises a Wheatstone bridge formed by arranging a plurality of piezoresistors and is used for collecting heart sound signals; the electrocardio acquisition circuit and the bionic cilium heart sound sensor are packaged in an acoustic impedance matching mode, and the electrocardio acquisition circuit comprises an electrocardio acquisition electrode plate, a power supply voltage stabilization module, an instrument amplification module, a band-pass filtering module and a notch filter and is used for acquiring and conditioning electrocardio signals; the signal fusion model comprises a Resnet18 feature extractor, an encoder and a mutual cross attention mechanism, and is used for performing feature extraction, linear transformation and feature deep fusion on the collected electrocardiosignals and electrocardiosignals and outputting a signal classification result.

The invention provides a fetal hypoxia early risk identification method and system based on a large pulse pressure phenotype, and relates to the technical field of biomedical signal processing and heart sound signal analyse.The method comprises the steps that fetal heart sound signals and body surface vibration signals at the abdominal wall of a pregnant woman are collected through a multi-channel sensing array; a preprocessing means is adopted to improve the signal-to-noise ratio of a target signal and suppress an interference signal; in the sliding time window, a large pulse pressure phenotypic feature set and heart sound time phase parameters are extracted; based on the combined criterion, fetal hypoxia risk judgment is carried out; and outputting the graded early warning information, and supporting the playback of the risk event fragment. According to the method, fetal real heart sound mechanical vibration serves as an information source, hypoxia early warning is achieved based on a large pulse pressure phenotype, individualized base lines (including day and night layering) and gestational week self-adaptive correction are supported, the anti-interference capacity is high, the 7 * 24-hour continuous monitoring requirement in a hospital / family can be met, and the sensitivity and specificity of hypoxia recognition are improved.

The invention discloses a multi-voice separation method based on a lightweight dual-path Transform network, and the method comprises the steps: collecting audio multi-voice data, and carrying out the preprocessing of the data, and forming a data set; the method comprises the following steps: constructing a dual-path Transform network model DPTNet, and introducing a recurrent neural network to optimize the dual-path Transform network model DPTNet; and training the dual-path network model DPTNet, and performing engineering deployment based on the trained model. The method is beneficial to obtaining higher-quality audio fingerprint recognition capability, sound source separation capability and voice enhancement function, can be used for tracking and positioning the position of a sound source, helps positioning and tracking related applications, can be expanded to the medical field, can be used for heart sound segmentation, namely, recognition of specific signals of the heart, and can be applied to the field of medical science. The method helps to diagnose cardiovascular and other medical problems, and has technical innovation and practical application value.

The invention discloses a multi-feature fusion heart sound classification method based on a channel attention mechanism, and belongs to the technical field of signal processing and heart disease diagnosis. The method comprises the steps that collected original heart sound records are preprocessed, a self-adaptive two-stage filtering method based on local variance and global variance is adopted, a wavelet denoising technology is combined, background noise is effectively removed, high-quality heart sound signals are extracted, and meanwhile heart sound events are accurately detected through a double-threshold segmentation method; fusing information of different dimensions to enrich the expression of the heart sound signal; a deep learning architecture fusing a convolutional recurrent neural network and a convolutional neural network is constructed, a channel attention mechanism is introduced, the fusion weight of each sub-network is dynamically adjusted according to the importance of features, and the classification accuracy is improved. According to the method, the quality of heart sound signals can be effectively improved, multi-domain features are fully fused, meanwhile, the discrimination ability of the model for heart sound categories is enhanced, and therefore the heart sound diagnosis precision is improved.

The invention discloses a multi-person electrocardiogram reconstruction method based on a millimeter-wave radar, which is applied to the technical field of millimeter-wave radar detection, and aims at solving the problem that in the prior art, only simple heart rate estimation of a plurality of targets is considered, but the estimation of heartbeat signals is not involved. The method comprises the following steps: firstly, converting a radar phonocardiogram into a frequency domain signal; extracting a top-k frequency component and converting the top-k frequency component into a time domain signal; then, multi-head cross attention and multi-head self-attention mechanisms are used for carrying out self-correlation reconstruction on the signals; the obtained output is then input into a frequency Gaussian weighting module, and then a reconstructed ECG signal is output. Compared with traditional heartbeat information analysis of multi-target signals, more accurate electrocardiogram reconstruction information can be provided.

According to the arteriosclerosis detection method and device, the upper arm blood pressure is obtained based on the heart and ankle vascular index CAVI; systolic pressure Ps and diastolic pressure Pd are obtained; calculating to obtain a pulse pressure difference delta P, wherein the pulse pressure difference delta P = systolic pressure Ps-diastolic pressure Pd; measuring to obtain the distance Lca from the heart aortic valve to the ankle; measuring to obtain the time difference Tca of the pulse wave propagating from the heart to the ankle; a heart and ankle vascular index CAVI is obtained through calculation according to the following formula; # imgabs0 # is used for measuring and obtaining a pulse wave starting point time point TA1 of the ankle; measuring a pulse starting point TA2 of brachial artery pulse waves, and calculating TA1 to TA2 to obtain a time difference Tba; measuring to obtain a heart sound signal of cardiac pulsation, and obtaining a time difference T2 between a second heart sound and a first incision of the brachial artery; the time difference Tca is equal to the sum of the time difference Tba and the time difference T2. At least three blood pressure measuring devices and at least three electrocardio detection electrodes are arranged; the heart sound detection module is in electric signal connection with the heart sound detection sensor through a signal line; the heart sound detection sensor is used for being placed at the heart position of a detected person to obtain heart sound signals.

The invention discloses a non-invasive continuous dynamic blood pressure detector data analysis method, and relates to the technical field of cardiovascular and cerebrovascular diseases, skin bio-electricity signals, heart sound signals and micro-motion signals are collected, the signals collected in the step S1 are preprocessed and sampled and aligned, a deep neural network is utilized to extract time sequence features, a convolutional neural network is utilized to extract spatial features, and the time sequence features are extracted; according to the method, blood pressure related features are extracted through a multi-source signal acquisition mechanism in combination with a deep neural network and a convolutional neural network, the blood pressure related features are fused into a high-precision blood pressure prediction model, and continuous dynamic blood pressure prediction can be realized without depending on a cuff and an invasive sensor; the system is suitable for home health management, chronic disease follow-up visit and remote medical treatment, and has wide clinical application and industrialization potential.

The invention provides a coronary artery stenosis degree identification method and system based on electrocardio and heart sound signals, and belongs to the technical field of physiological signal analysis. The method comprises the steps that electrocardiosignals and heart sound signals which are to be recognized and synchronously collected are obtained and preprocessed; respectively extracting an amplitude sequence and an interval sequence of the preprocessed electrocardiosignal and heart sound signal; extracting multi-domain features and graphical features based on the amplitude sequence and the interval sequence, and constructing a joint feature set; and inputting the joint feature set into a trained recognition model to obtain a coronary artery stenosis degree recognition result. According to the coronary artery stenosis degree evaluation method, the performance of coronary artery stenosis on two sequences of electrocardio and heart sound signals, amplitude intensity and interval rhythm is fully considered, multi-domain features such as time domain, frequency domain and nonlinearity and detail texture features of a graphical method are extracted, an accurate identification and classification result is obtained, and the coronary artery stenosis degree is accurately evaluated.

Disclosed is a method of constructing long and short-range dependency network learning model: that includes obtaining heart sound data having plurality of audio files; preprocessing the plurality of audio files; extracting Mel-Frequency Cepstral Coefficients (MFCCs) from the preprocessed plurality of audio files; restructuring extracted MFCCs into an input layer of the long and short-range dependency network learning model; implementing two or more LSTM network layers with a dropout; and employing a softmax activation function to a final output layer. Disclosed also is a method of classifying heart rate sound data.

The invention discloses a multi-modal electrocardiosignal and arrhythmia recognition system and method. The system comprises a signal acquisition module, a preprocessing module, a space-time alignment module, a feature extraction module, a self-adaptive fusion module, a multi-modal fusion module, a classification risk assessment module and a visual report module. The recognition method comprises the following steps: S1, synchronously acquiring an electrocardiosignal and a heart sound signal, and preprocessing the electrocardiosignal and the heart sound signal; s2, establishing a time corresponding relation between the electrical activity and the mechanical activity; s3, calculating a mechanical shrinkage efficiency index; s4, evaluating the quality of the electrocardiosignal and the heart sound signal in real time, and dynamically adjusting contribution weights of the electrocardiosignal and the heart sound signal in a fusion process according to the quality; s5, adopting a multi-head attention mechanism to generate a joint feature vector for classification; s6, inputting the joint feature vector into a classification network model, and outputting an arrhythmia type and a quantitative risk level at the same time by the classification network model; and S7, generating a visual atlas and a structured diagnosis report.

The present disclosure relates to a heart sound signal processing method, device and wearable device, the method comprising: in the case that the signal quality of a first heart sound signal does not meet a preset requirement, continuously segmenting the first heart sound signal into a plurality of first sub-signals and continuously segmenting a second heart sound signal into a plurality of second sub-signals; determining a weight coefficient of the frequency domain signal corresponding to each frequency value of the first sub-signals and the second sub-signals according to the phase difference of the frequency domain signal corresponding to each frequency value; determining a fused frequency domain signal corresponding to each frequency value based on each window, according to the frequency domain signal corresponding to each frequency value of the first sub-signals and the second sub-signals and the weight coefficient of the frequency domain signal corresponding to each frequency value; determining a time domain signal of each sampling point based on each window, according to the fused frequency domain signal corresponding to each frequency value; and generating a reconstructed heart sound signal according to the time domain signal of each sampling point of each window.

A method and apparatus for predicting blood pressure, an electronic device, and a readable storage medium, relating to the technical field of medical treatment. The method for predicting blood pressure comprises: synchronously acquiring physiological signals, wherein the physiological signals comprise an electrocardiographic signal, a pulse signal, and a heart sound signal; performing feature extraction on the physiological signals to obtain signal features, wherein the signal features comprise an pre-ejection period duration and a pulse transmission time; and inputting the signal features into a pre-trained blood pressure prediction model, so that the blood pressure prediction model outputs a blood pressure prediction result, thereby improving the prediction accuracy of blood pressure.

The utility model discloses a kind of postoperative atrial fibrillation monitoring alarm systems, it is related to the technical field of atrial fibrillation monitoring, and it includes: main control module, detection module, storage module, display module, input-output module and alarm module;Detection module detects the electrocardiosignal, pulse voltage signal and heart sound signal of postoperative patient, sends to main control module;Main control module carries out sampling to electrocardiosignal, pulse voltage signal and heart sound signal, and stores to storage module;According to the instruction of input-output module, the signal after selection sampling is displayed through display module, and the signal stored is selected from storage module and is displayed through display module;Threshold comparison unit of main control module sends signal to alarm module according to heart rate signal, pulse signal, heart sound signal and threshold value, and alarm module sends alarm prompt.The utility model can monitor the sign data of postoperative patient and send alarm, remind medical staff to handle in time, reduce the influence of postoperative atrial fibrillation on patient recovery.

The invention, which relates to the technical field of abnormal identification data processing, discloses an abnormal identification system and method for a child heart disease heart sound signal, and the system comprises a child heart disease heart sound signal data acquisition and processing module, a child heart disease heart sound signal data analysis module, a comprehensive analysis module and an optimization and adjustment module. According to the invention, the heart sound signals of the child heart disease are collected through the sensor, the synchronism and quality of the heart sound signals of the child heart disease are evaluated after preprocessing, and the heart sound signals of the child heart disease are comprehensively analyzed in combination with the artifact detection rate, so that the abnormal recognition process of the heart sound signals of the child heart disease is optimized; therefore, the accuracy of the abnormal recognition system for the heart sound signals of the child heart disease is improved, and the problem of insufficient accuracy of the abnormal recognition system for the heart sound signals of the child heart disease in the prior art is solved.

The invention discloses a multi-sensor monitoring system and method based on time alignment, and belongs to the technical field of multi-sensor monitoring, and the system comprises a sensor collection module which is used for obtaining an electrocardiosignal, a photoelectric volume pulse wave signal, a heart sound signal and a respiratory impedance signal containing a main clock receiving timestamp; the time alignment module is used for performing acquisition time alignment on the electrocardiosignal, the pulse oximeter signal, the heart sound signal and the respiratory impedance signal containing the main clock receiving timestamp to form the electrocardiosignal, the pulse oximeter signal, the heart sound signal and the respiratory impedance signal which are in time alignment; the multi-modal fusion analysis module is used for extracting local morphological features in the electrocardiosignal, the pulse oximeter signal, the heart sound signal and the respiratory impedance signal after time alignment to form a feature map, and extracting dynamic weights between different signals and between different time points / features in the same signal; according to the invention, the interpretability and accuracy of judgment are improved.

A kind of heart sound sensing structure and its preparation method, the structure includes double SOI substrate, center mass, elastic support beam, piezoresistive sensing unit and capacitive sensing unit.The front surface of center mass is provided with additional mass layer, and the back surface is kept bottom layer silicon to increase thickness;Elastic support beam connects mass and frame;Piezoresistive sensing unit is composed of four piezoresistors to form wheatstone bridge, and capacitive sensing unit includes movable and fixed comb-tooth capacitive plate crossing each other.When the sound pressure gradient of heart sound signal drives mass to deviate, elastic support beam bends to change piezoresistive value, and at the same time, comb-tooth capacitive plate overlapping area changes to cause capacitive value change, to realize piezoresistive and capacitive dual-mode synchronous detection.Two kinds of detection mechanisms calibrate each other, which significantly improves the sensitivity and anti-interference ability of low-frequency heart sound signal.The preparation method is based on double SOI substrate, integrated by ion implantation, etching, metal deposition and 3D printing process, suitable for miniaturized wearable heart sound monitoring application.

1. Name of the Design Product: Heart Sound Detector (Wireless). 2. Use of the Design Product: For detecting heart sounds and lung sounds. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Usage State Diagram. 5. Other Situations Requiring Explanation - Component Description: Component 1 is the main unit of the wireless heart sound detector; Component 2 is the base.

The invention relates to the technical field of vital sign monitoring, and discloses a non-contact vital sign monitoring method and system applied to a dental chair, and the method comprises the steps: synchronously collecting a physiological acoustic signal of a headrest and a mechanical vibration signal of a chair back, determining a reference time point of each cardiac cycle according to a first heart sound event of the acoustic signal, the method comprises the following steps of: acquiring a mechanical vibration signal, separating and subtracting a low-frequency breathing baseline of the mechanical vibration signal from the mechanical vibration signal for correction, determining a mechanical response time point according to a reference time point, and further calculating a time interval between the mechanical response time point and the mechanical response time point. The obtained time interval parameter gets rid of the influence of mechanical transfer function drift caused by posture change of a patient, so that measurement artifacts of a traditional vibration signal in amplitude and form are avoided, and stable and reliable vital sign state evaluation is provided for the diagnosis and treatment process.

The present invention discloses a heart sound and coronary artery murmur detection device, comprising: a pickup placed at the projection positions of the coronary arteries on the body surface, the projection positions including the positions near the sternal body in the third intercostal space on the left side of the human body, the positions near the sternal body in the fourth intercostal space, the positions near the sternal body in the third intercostal space on the right side of the human body, and the positions near the sternal body in the fourth intercostal space, the pickup being used for collecting a first sound signal; a filter amplification circuit, the input end of which is connected to the output end of the pickup, and which is used for extracting and amplifying the coronary artery murmur signal to form a first coronary artery murmur signal. By using the above device to synchronously collect the first sound signals at multiple positions, it is ensured that the murmurs in the complete cardiac coronary artery region are effectively captured; by extracting and processing the coronary artery murmur signal through the filter amplification circuit, it is convenient to realize the early screening of coronary heart disease according to the coronary artery murmur signal. The present invention also discloses a heart sound and coronary artery murmur detection method.