1790results about "Stethoscope" patented technology

SOLUTION: To provide a cardiac monitoring system and a method for using such a system, in which preferable embodiments of the system and method detecting cardiac information through a wearable device and then recording the same, and extracting data features from the recorded cardiac information, the extracted data features being analyzed and used for clinical diagnosis.EFFECT: A biological monitoring system can smooth and / or enhance patient experience, for achieving more precise and timely diagnosis of cardiac arrhythmia.SELECTED DRAWING: Figure 11

A handheld medical diagnostic device integrates at least seven sensor modules, including a high-magnification camera, otoscope camera, stethoscope, infrared thermometer, EKG, pulse oximeter, body composition monitor, glucometer, and / or hematology analyzer. The device features a system-on-chip (SoC) processor for intelligent data management, edge computing, and cloud-based AI processing, while applying privacy-preserving techniques. A wireless transceiver enables data transmission and reception between the sensors, edge compute nodes, and cloud platforms. The ergonomically designed housing includes a display with a force-sensitive layer for user input and navigation. The high-magnification camera module offers 300× magnification for visualizing blood cells and skin, with an annular LED array, encryption engine, and physically unclonable function (PUF) circuit. The otoscope camera module features a narrow profile for ear, nose, and throat imaging, with an annular LED array.

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

The invention relates to the field of breath sound recognition, and particularly discloses a breath sound classification method and system based on a Mel spectrogram, which utilizes a breath sound classification model with a novel network structure to improve the sensitivity and specificity of breath sound classification. Particularly, the breath sound classification model with the novel network structure comprises an audio frequency spectrogram converter network, a multi-scale feature extraction network, a time sequence feature extraction network and a linear output layer network, the multi-scale feature extraction network and the time sequence feature extraction network form a feature coding and decoding architecture to carry out global feature and local feature reinforcement learning on breath sound spectrum features output by the audio frequency spectrogram converter network, so that sensitivity and specificity of breath sound classification are improved.

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

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

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

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

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

The invention provides a temporomandibular joint health state monitoring system and method based on joint vibration information, the system comprises a pasting touch type sound sensor, a microcontroller and an interactive display terminal, the pasting touch type sound sensor is used for pasting on a temporomandibular joint, obtaining a three-dimensional joint vibration analog signal, and sending the three-dimensional joint vibration analog signal to the microcontroller; the microcontroller carries out signal amplification, filtering processing and noise elimination, converts the signals into digital signals which can be identified and stored by the display terminal, and sends the digital signals to the interactive display terminal; the interactive display terminal is used for realizing real-time visual output of a three-dimensional joint vibration oscillogram and realizing an interaction function, and judging the health state of the temporomandibular joint based on an input maximum mandibular movement range, existence or absence of pain and joint noise parameters; on the basis of more comprehensive joint vibration information, real-time quantitative monitoring of the health state of the temporomandibular joint can be more accurately achieved, and a doctor can effectively and preliminarily screen the health state of the joint and a user can conduct self-monitoring at home conveniently.

The invention discloses an intelligent fetal heart monitoring method and system for the obstetrical department, and relates to the technical field of medical monitoring. A multi-modal sensing monitoring unit, an intelligent data processing technology, a remote data analysis platform and an intelligent equipment application program are integrated. According to the system, fetal heart data are comprehensively collected through fetal heart sound, vibration, temperature and other multi-dimensional sensors, the data are cleaned, compressed, encrypted and processed through an intelligent algorithm, and the accuracy and safety of the data are ensured. And the remote data analysis platform analyzes the fetal heart data in real time and provides personalized medical suggestions. The intelligent device application program provides convenient user interaction and supports custom monitoring plan and data cloud synchronization. On the whole, comprehensiveness, accuracy and real-time performance of fetal heart monitoring are achieved, efficiency and safety of maternal and infant health monitoring are improved, and a powerful health guarantee tool is provided for medical staff and pregnant women.

Systems and methods for contactless monitoring of cardiac activities include a vibration sensor operable to collect audio input signals of a user and a processor. The processor is operable to extract, using an audio model, cardiac sound data from the audio input signals, and transfer, using a trained neural network, the cardiac sound data into the simulated electrocardiogram (ECG) data based on a weak alignment between cardiac sounds and ECG.

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 heart sound signal noise detection method based on a multi-scale convolutional network. The method comprises the following steps: collecting heart sound signal information; cutting into segments with fixed length as network input; then, the cut heart sound fragments are preprocessed; evaluating the quality of the heart sound fragment based on the spectral density ratio, and correcting the label of the low-quality fragment; generating a spectrogram; effective features in spectrograms of different scales are extracted through a convolutional neural network of a hierarchical multi-scale structure, and accurate recognition and classification of heart noise are achieved; training the multi-scale convolutional neural network; and according to an output result of the multi-scale convolutional neural network, outputting a diagnosis result of the cardiac murmur. According to the method, the hierarchical multi-scale convolutional neural network is introduced, so that multi-scale features are effectively fused, and the accuracy and robustness of heart sound signal noise detection and clinical result classification are improved. And the quality of the heart sound fragments is evaluated based on the spectral density ratio, and the labels of the low-quality fragments are corrected, so that the classification performance is further improved.

The invention relates to the technical field of ultrasonic measurement, in particular to a heart parameter measuring method and system. According to the heart parameter measurement method, the position of the heart is acquired, and the probe is finely positioned according to the position of the heart, so that the probe can accurately and quickly find and acquire the ultrasonic image at the standard section of the heart; then capturing a target standard section in the ultrasonic image by adopting a pre-trained self-supervised deep learning model; and finally, carrying out key frame extraction on the 2D echocardiogram video so as to measure a heart structure in a key frame to obtain heart structure parameters, cutting out a region of interest in the Doppler echocardiogram, and carrying out segmentation processing on the region of interest to obtain functional parameters of the heart. By means of the mode, various parameters of the heart can be accurately and efficiently obtained, full automation is achieved in the whole process, and the accuracy and consistency of various parameter test results of the heart are guaranteed.

The invention discloses a heart sound feature extraction, decomposition, reconstruction and quantification method which is characterized by comprising the following steps: S1, collecting heart sound signals; s2, carrying out noise reduction pretreatment; s3, carrying out wavelet decomposition and reconstruction on the heart sound signals; s4, calculating signal power spectral density and normalizing an energy spectrum; s5, calculating a signal energy entropy value; and S6, analyzing the correlation between the energy entropy and the defect size. According to the method, visualization of the heart sound digitized signals is achieved through multiple steps, the heart sound digitized signal decomposition and reconstruction method is provided, the waveform distribution condition of the recombined heart sound is visualized, the accuracy is high, and the reliability is high.

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

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

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

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

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

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

Various methods and systems are provided for an automated clinical exam workflow. In one example, method comprises performing a signal quality check of an electronic stethoscope at a first recording location on a subject, recording physiological data for an exam at the first recording location via the electronic stethoscope in response to the signal quality check satisfying a quality threshold, and outputting a signal quality alert in response to the signal quality check not satisfying the quality threshold. In this way, clinically relevant data may be obtained with reduced user effort and fewer manual inputs.

The invention provides a heart conduction health degree assessment method, medium and system, and belongs to the technical field of electrical digital data processing.The heart conduction health degree assessment method comprises the steps that firstly, heart conduction signals of multiple testers and expert assessment results are collected; splitting the signals by using an empirical mode decomposition method to obtain a standard signal matrix and a non-standard signal matrix; then calculating the wavelet feature of the standard signal and the Fourier feature of the non-standard signal; based on the features, an improved convolutional neural network model is constructed, including a spatial pyramid pooling layer, an attention mechanism, residual connection and multi-scale feature fusion. After being trained, the model can be used for evaluating the health degree of a to-be-evaluated heart conduction signal. According to the method, the time-frequency characteristic of the signal is fully utilized, more accurate health condition assessment is realized through improvement of the deep learning model, and the problem that the overall health condition is difficult to comprehensively and objectively assess since most of the prior art only focuses on the local characteristic of the heart conduction system is solved.

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

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

The invention provides a blood pressure detection and classification system and method based on a neural network, a medium and a terminal. Mel-frequency cepstral coefficient feature extraction, first heart sound time domain feature extraction and second heart sound time domain feature extraction are performed on acquired heart sound signal real-time data; the extracted Mel frequency cepstral coefficient feature data, the first heart sound time domain feature data and the second heart sound time domain feature data are used as model features to be input, time domain features and frequency domain features are included, and comprehensive coverage of time domain information and frequency domain information is achieved. The multi-input time domain convolutional neural network model is used for processing the three feature inputs and fusing the three feature inputs, the current blood pressure state of the user can be accurately detected, convenience and rapidness are achieved, and the result is accurate. The algorithm for extracting the frame where the heart sound peak point is located based on the MFCC0 is achieved, the influence of noise can be effectively avoided, the algorithm is simpler and more accurate, meanwhile, higher fault tolerance is achieved, and the first heart sound and the second heart sound can be accurately distinguished.