202 results about "Cardiac sounds" patented technology

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 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-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 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 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 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.

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 disclosure provides a subclinical valve leaflet thrombosis auxiliary identification method, system and signal acquisition device after transcatheter aortic valve replacement, belonging to the technical field of heart information acquisition and identification. The subclinical valve leaflet thrombosis auxiliary identification method is used for auxiliary diagnosis of subclinical valve leaflet thrombosis after transcatheter aortic valve replacement, comprising: collecting a heart sound signal, processing the heart sound signal, obtaining preset parameters of the heart sound signal, and the preset parameters at least including time, frequency and energy; determining a systolic period according to the heart sound signal; identifying the highest energy point in the characteristic interval of the systolic period; obtaining the characteristic frequency corresponding to the highest energy point, and judging whether the characteristic frequency is greater than the threshold frequency, if yes, marking the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, marking the heart sound signal as a normal heart sound signal. The method solves the problem that the subclinical valve leaflet thrombosis detection mainly uses multi-dimensional computed tomography, which leads to limited scope of application.

A cardiac diagnostic apparatus 100 includes a phonocardiography (PCG) sensor 108 for sensing heart sounds and a mechanocardiography (MCG) sensor 109 for sensing heart induced motion, for example due t

The invention relates to the technical field of heart sound signal processing, and particularly discloses a heart sound signal respiration interference suppression method and system based on multi-source sensing fusion, and the method comprises the steps: constructing a multi-source synchronous signal set through synchronously collecting heart sound signals and respiration monitoring signals; separating a heart sound principal component, a breathing interference component and a noise component by utilizing an adaptive modal decomposition and multi-dimensional feature clustering technology; constructing a time-frequency domain interference template through multi-scale cross-correlation analysis and phase registration by taking a respiration monitoring signal as a reference; based on the template, adopting time-varying gain control and an iterative optimization strategy to realize accurate suppression of respiratory interference, and generating a pure heart sound signal; and through multi-resolution feature enhancement and multi-index quantitative evaluation, generating a purity report containing a signal quality grade and a clinical applicability scheme.

Methods and systems for monitoring heart valve operation using signals detected by a heart sound sensor includes memory to store program instructions and one or more processors that, when executing the program instructions receive seismocardiography (SCG) signals detected by a heart sound sensor along an axis. The SCG signals include heart sound signals for a series of heartbeats over a first time period. The SCG signals are segmented into SCG segments for corresponding heartbeats within the series of heartbeats over the first time period. A template is calculated based on a first subset of the SCG segments. At least a portion of a second subset of the SCG segments are compared to the template to determine matching scores, and an alert is generated in response to a select number of the second subset of SCG segments having the matching scores that satisfy a matching threshold.

This utility model relates to a blood pressure monitor, disclosing a multifunctional blood pressure monitor including a main unit and a secondary unit. A cuff is installed at the lower end of the main unit, and a first main control board is installed inside the main unit. The secondary unit contains a second main control board and a battery, which is connected to and powers the second main control board. Electrodes are also installed on the secondary unit and connected to the second main control board. The main unit and the secondary unit are detachably connected. When the secondary unit is mounted on the main unit, the battery is connected to the first main control board via the second main control board. The battery in the secondary unit not only ensures the normal operation of its own ECG and heart sound measurement functions but also, when mounted on the main unit, powers the first main control board of the main unit via the second main control board. This power supply mode solves the problem of separate power supply for the main unit and achieves the organic integration of blood pressure, ECG, and heart sound measurement functions. Users do not need to prepare multiple power supply devices, reducing operating costs.

The application discloses an interpretable heart sound anomaly recognition method and system based on a fractional domain Fourier transform, which comprises preprocessing, feature extraction, model establishment and model interpretation. The preprocessing comprises shearing, downsampling, filtering, amplitude normalization, heart cycle segmentation, frame segmentation and windowing in sequence; the feature extraction is configured to firstly perform fractional domain Fourier transform on the preprocessed heart sound, then extract frame-level Shannon entropy features of one-dimensional fractional domain heart sound signals, and calculate 13 statistical functions on the frame-level features as final features; the model establishment selects an XGBoost classifier; and the model interpretation selects a SHAP (SHapley Additive exPlanation) interpretation model. The application is easy to realize, simple in method, low in feature dimension, fast in model fitting, and has model prediction interpretability.

This invention discloses a non-contact electrocardiogram (ECG) measurement method using millimeter-wave radar. First, the original chest wall displacement signal acquired by the millimeter-wave radar is preprocessed to obtain a displacement feature branch input. Then, the original displacement signal undergoes cardiac sound band enhancement, wavelet denoising, and envelope extraction to obtain radar cardiac sound features. A hidden semi-Markov model is constructed to model the states of the first heart sound, systole, second heart sound, and diastole in the cardiac cycle, obtaining the posterior probability of the state at each time point. Next, the displacement features, radar cardiac sound features, and state probability features are input into a multi-branch convolutional fusion network, combined with a bidirectional long short-term memory network to achieve temporal correlation modeling, finally outputting the reconstructed human ECG result. This invention achieves non-contact ECG measurement without skin electrode contact by fusing the original mechanical displacement of the chest wall, the mechanical vibration features of the heart sounds, and the explicit state prior information of the cardiac cycle.

The invention provides a heart sound classification method based on an evolutionary fuzzy system. The heart sound classification method comprises the following steps that S1, obtained sample heart sound signals are preprocessed; s2, feature extraction is carried out on the preprocessed heart sound signals, and heart sound signal features comprise time domain features and frequency domain features; s3, constructing an evolutionary fuzzy system, and inputting the heart sound signal features as samples into a fuzzy evolutionary system to train the evolutionary fuzzy system; and S4, acquiring heart sound signals in real time, preprocessing the real-time heart sound signals, then performing feature extraction, and inputting the features of the real-time heart sound signals into the trained evolutionary fuzzy system to obtain a heart cause signal dichotomy result.

An in-ear device (IED) of a wearable system captures audio data from within the ear canal of a user indicative of the user's heartbeat. The IED when worn occludes the user's ear canal, causing amplification of low frequency sounds due to the occlusion effect, and improving the ability of the acoustic sensor to detect the user's heartbeat sounds. The wearable system classifies the audio data to identify portions of the audio data corresponding to a first heart sound and a second heart sound, which correspond to different portions of the heartbeat of the user. The wearable system estimates a blood pressure level of the user based upon the identified heart sounds.

This invention discloses a method for phase synchronization of multimodal data from heart sounds, electrocardiograms (ECG), and echocardiograms, belonging to the field of medical image processing technology. The method includes the following steps: A1. Acquiring original echocardiogram video images and independently recorded heart sound audio data; A2. Preprocessing the acquired data and automatically constructing samples; A3. Constructing a dual-path neural network architecture and extracting ECG waveforms to obtain a digitized ECG signal sequence; A4. Identifying periodic feature points of heart sounds and ECGs based on the digitized ECG signals; A5. Aligning asynchronous data based on linear phase resampling. This invention uses a linear interpolation algorithm to forcibly map video frames, electrical signals, and heart sound signals at different sampling rates to a unified phase coordinate system, ultimately achieving absolute synchronization of the three in terms of the physiological logic of cardiac pulsation, eliminating the influence of individual heart rate differences on data alignment.

Systems (100), computer-readable media, and methods for detecting a first indicator of a pericardium-related issue based, at least in part, on first heart sounds measurements; detecting a second indicator of the pericardium-related issue based, at least in part, on one or more of the following: posture, restlessness, respiratory activity, cardiac activation signals, oxygen saturation, and second heart sounds measurements; and generating an alert in response to detecting the first indicator and the second indicator.

This technology provides highly accurate estimation of heart rate intervals without requiring prior machine learning. [Solution] The disclosed method includes (a) a step of extracting frequency components of the heart sound band from a heart sound signal including the first heart sound and the second heart sound, and generating a heart sound band waveform SBW by integrating the amplitude components of the heart sound band at each time; (b) a step of determining the time interval TI between the first heart sound and the second heart sound; and (c) a step of detecting the interval between the appearance of peak pairs having a time interval TI in the heart sound band waveform SBW to determine the heartbeat interval.

The application relates to the technical field of disease screening, in particular to an abnormal heart sound detection method based on a space-time attention feature fusion model. The abnormal heart sound detection method first acquires heart sound signal data, pre-processes the heart sound signal data, and obtains pre-processed heart sound signal data; each heart sound signal feature data is determined by performing feature extraction on the pre-processed heart sound signal data; each heart sound signal feature data is subjected to multi-source feature fusion and heart sound classification through a constructed CNN-TCN-Attention network model, and a classification result of the heart sound signal data is obtained. The application directly extracts features from the heart sound signal data, utilizes the CNN-TCN-Attention network model to perform heart sound signal classification, effectively improves the accuracy of abnormal heart sound recognition, and is mainly applied to the field of abnormal heart sound detection.

The invention discloses a lung sound recognition method based on period synchronization self-supervised cardiopulmonary decoupling. The method comprises the following steps: acquiring a single-channel auscultation audio training sample set and to-be-recognized audio, and preprocessing the single-channel auscultation audio training sample set and the to-be-recognized audio; performing time-frequency analysis on the preprocessed signal to obtain a time-frequency characteristic; calculating cardiac cycle information representation according to the time-frequency characteristics; constructing a cardiopulmonary decoupling recognition model containing coding, decoupling, decoding reconstruction and classification networks, and optimizing model parameters by using a training target containing reconstruction consistency, period synchronization, and characterization decoupling and classification constraint; and during identification, inputting the time-frequency characteristics extracted from the audio to be identified and the cardiac cycle information representation into the trained model, and outputting a lung sound identification result. The method has the advantages that heart sound interference can be restrained under the single-channel condition, recognition accuracy and robustness are improved, training is completed without heart sound labeling, and labeling cost is reduced.

This application relates to the field of heart sound signal technology, and discloses a heart rate calculation method, device, medium, and electronic device. The method includes: preprocessing an input heart sound signal to obtain an envelope signal; generating a heart sound signal peak sequence containing heart sound components based on a preset adaptive threshold and the envelope signal; obtaining multiple cardiac cycles of the envelope signal based on the heart sound signal peak sequence; selecting a preset number of cardiac cycles as target cardiac cycles from the heart sound signal peak sequence; calculating the heart rate value corresponding to the heart sound signal based on the average cycle duration of the target cardiac cycles; or calculating the heart rate value corresponding to the heart sound signal based on the time interval between two adjacent first heart sound signals or the time interval between two adjacent second heart sound signals. The heart rate calculation method provided by this application can solve the problem of inaccurate heart rate values ​​calculated due to false detections and missed detections, which is unavoidable in traditional heart rate calculation schemes.