317 results about "Cardiac sounds" patented technology

The invention belongs to the technical field of medical signal processing, and particularly relates to a heart sound and electrocardio combined coronary heart disease detection method based on time-frequency fusion, which comprises the following steps: S1, performing data preprocessing on PCG and ECG original signals, including noise reduction, baseline drift elimination, power line interference elimination and the like, and performing normalization processing on the PCG and ECG signals to obtain a normalized PCG and ECG signal; a Welch method is adopted to generate power spectrum signals of heart sound and electrocardio, and time domain signals and power spectrum signals of PCG and ECG are segmented into equal-length fragments. In order to effectively solve the problem of inaccurate diagnosis caused by poor single-modal data quality, a heart sound and electrocardio dynamic fusion module is constructed based on a time-frequency fusion result of two signals, dynamic weight distribution is carried out, a proper classifier is selected for classification, and the diagnosis accuracy is improved by supplementing frequency domain information. And the weight is distributed according to the contribution of the heart sound and the electrocardiosignal, so that the detection and classification result of the coronary heart disease is more accurate.

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 relates to the technical field of heart sound signal processing, and discloses a heart sound feature recognition method based on wavelet spectral analysis. The method comprises the following steps: collecting heart sound signal data and carrying out signal filtering and segmentation processing to obtain segmented cardiac cycle signals; performing n-level discrete wavelet transform decomposition to obtain an approximation coefficient and n detail coefficients; performing multi-dimensional statistical feature calculation to obtain a multi-dimensional feature vector; performing principal component analysis dimension reduction processing to obtain a dimension reduction feature vector; fitting feature distribution by using a Gaussian mixture model and introducing a dynamic adjustment factor to obtain an adaptive classification threshold; the hidden Markov model is constructed through the dimension reduction feature vectors, the likelihood probability is calculated through a forward algorithm, the likelihood probability is compared with a self-adaptive classification threshold value, and the heart sound feature recognition result is output. The time-frequency features of the heart sound signals are fully utilized, the feature distinction degree and the recognition accuracy are improved, and the recognition accuracy is improved. And meanwhile, the method has good robustness and generalization ability.

The invention discloses a heart sound-electrocardio multi-modal fusion detection method based on federal learning. According to the method, collaborative fusion of multi-modal information of heart sound and electrocardio data is achieved through a cross-modal collaborative attention mechanism, the comprehensive performance of federal learning is effectively improved, and the detection accuracy of a federal model is remarkably improved; meanwhile, by introducing a weight distribution mechanism, the adaptability of the model to the imbalance of each modal data is enhanced; a differential privacy protection technology is introduced to enhance the safe aggregation of model parameters; and the method has better robustness and privacy protection in practical application.

The invention discloses an intelligent electronic stethoscope system based on wireless transmission and a data processing method thereof, and belongs to the technical field of electronic stethoscopes, the intelligent electronic stethoscope system comprises a heart sound acquisition module, a signal conditioning module, a power supply module, a wireless transmission module, an upper computer module and a database module; the microprocessor module serves as a core control unit, and the system can collect heart sound signals of a patient in real time and conduct amplification, filtering and spectrum analysis processing on the heart sound signals. The processed heart sound data is transmitted to a mobile device or a computer in real time through a wireless transmission module, so that doctors and users can check heart sound waveforms and related data conveniently; the system also supports the uploading of the data to a cloud database for remote diagnosis and query; the method not only can meet the requirements of modern medical treatment for high efficiency and convenience, but also can play an important role in improving the diagnosis precision and the remote diagnosis capability and reducing the infection risk.

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.

An electronic device includes: a transmission unit that transmits a transmission wave; a receiving unit that receives a reflected wave of the transmission wave from the target; and a signal processing unit that detects the distance, direction, and speed of the target on the basis of a converted signal obtained by Fourier-converting a beat signal of the transmission wave and the reception wave. The signal processing unit extracts a signal component corresponding to heart sound vibration accompanying the heartbeat of the subject from the converted signal using a plurality of window functions, and performs frequency analysis on an envelope signal obtained by performing envelope processing on the signal component corresponding to the heart sound vibration. And performing calculation processing of a statistical result on a frequency analysis result, and outputting time sequence data of the heartbeat frequency of the object based on a calculation result.

The invention provides an equipment detection method and device, an upper computer and a system, and the method comprises the steps: playing a noise signal through a loudspeaker under the condition that a bone conduction sensor of wearable equipment collects a heart sound signal of a wearer; determining an environmental noise signal according to a noise signal collected by a microphone at the wearable device; according to the heart sound signals collected by the bone conduction sensor, the interference degree of the environment noise signals on the wearable device is determined.

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 provides a non-contact heart sound monitoring method and system, a terminal and a medium, and particularly relates to the technical field of biomedical engineering.The non-contact heart sound monitoring method comprises the steps that lasers of different wavebands are used for conducting multi-point synchronous monitoring on the heart, and a defocus speckle image is obtained; performing signal extraction on the out-of-focus speckle image to obtain heart movement signals corresponding to multiple wavebands; and filtering the heart movement signals corresponding to the multiple wavebands to obtain heart sound signals. According to the scheme, the characteristic of out-of-focus independent interference imaging of the multi-band laser is utilized, multi-point synchronous monitoring is carried out on micro movement of the heart position in the thoracic cavity through the laser of different bands, the heart sound can be monitored in a non-contact mode, the requirement for measuring environment noise can be lowered, meanwhile, the detection precision is guaranteed, and the detection efficiency is improved. And the comfort of the target living body can be improved.

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 provides a heart sound classification method based on sample amplification and a noise attention network, which relates to the technical field of medical signal processing and artificial intelligence and comprises four steps of data set construction, data processing, heart sound classification model construction and classification result output. Noise outside the heart sound frequency range is filtered out through a band-pass filter and then input into a generative adversarial network to generate heart sound data, then a channel and space attention mechanism is introduced to facilitate extraction of meaningful features by a model, the model is enabled to converge more quickly, and then Gaussian noise with proper standard deviation is injected to obtain heart sound data. The robustness and generalization ability of the noise attention network with the channel and space attention mechanism are improved; along with gradual convergence of training, indexes such as accuracy, specificity and precision of the output cardiovascular disease types gradually tend to be stable, so that auxiliary diagnosis is provided during heart sound auscultation, the cardiovascular disease types are output, and a reliable basis is provided for heart sound auscultation results.

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.

This document discloses a computer-implemented method for non-invasive assessment of pulmonary arterial hypertension PH from heart sound signals, the method comprising the steps of: receiving sound signals acquired from a beating heart of a subject for a predetermined period of time; generating one or more 2D feature maps, the feature maps including a 2D feature map having the received sound signals, where a first axis of the map is arranged in time and a second axis of the map is arranged on each heartbeat; a pre-trained neural network is applied to associate the generated one or more 2D feature maps with a previously acquired training dataset and to generate training 2D feature maps for a set of PH subjects and a set of non-PH subjects, thus obtaining an indication of the presence of pulmonary arterial hypertension. The invention also discloses a training method and system of the neural network.

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.

The invention relates to the field of heart monitoring, in particular to a wearable device, method and system for collecting heart sound data and a storage medium. The wearable device is worn on the ear of a user and comprises a first sound collection module, a second sound collection module and a processing module. Wherein the first sound acquisition module is used for acquiring heart sound data of a user, and the second sound acquisition module is used for acquiring noise data around the user; and the processing module is used for determining whether the noise data meets a preset condition or not, and determining that the heart sound data is valid data under the condition that the noise data meets the preset condition.

The invention belongs to the technical field of computers, particularly relates to a heart sound signal denoising method based on an improved wavelet threshold value, and aims to solve the problem that a heart noise signal component and a heart sound signal component cannot be accurately determined by an IMF component obtained after a heart sound signal is decomposed through CEEMDAN. In the prior art, the selection of a threshold value and a threshold value function in a wavelet threshold value denoising process affects the denoising effect of a signal, when the threshold value is selected too large, some effective components in the heart sound signal are lost, and when the threshold value is selected too small, part of noise in the heart sound signal is not completely eliminated. The method comprises the following steps: decomposing heart sound signals by using CEEMDAN, defining noisy heart sound IMF components and heart sound signal IMF components by using de-trending fluctuation analysis, and de-noising the heart noise IMF components defined by a de-trending fluctuation analysis algorithm by using a wavelet threshold de-noising method of an improved wavelet threshold selection mode. And reconstructing to obtain a de-noised heart sound signal.

The invention provides a double-model fusion heart sound signal quality evaluation method and device, equipment and a medium, and relates to the technical field of computers. The double-model fused heart sound signal quality evaluation method comprises the following steps: acquiring a to-be-detected heart sound signal and the positions of a first heart sound and a second heart sound in the to-be-detected heart sound signal; based on the positions of the first heart sound and the second heart sound in the heart sound signal to be detected, extracting signal quality features from the heart sound signal to be detected; obtaining a first quality evaluation result based on the heart sound signal to be detected and a preset first quality evaluation model; taking the signal quality characteristics as input data of a preset second quality evaluation model to obtain a second quality evaluation result; wherein the first quality evaluation model and the second quality evaluation model are different in structure; and obtaining a heart sound signal quality evaluation result based on the first quality evaluation result and the second quality evaluation result.

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.