529results about "Stethoscope" patented technology

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

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

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

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

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

The present description relates generally to methods and systems for power management of a digital (e.g., electronic) stethoscope. In one example, an electronic stethoscope includes a chestpiece configured to be positioned on a patient, the chestpiece including a sensor, wherein the chestpiece further includes a computer processing unit (CPU) operatively coupled to a memory storing instructions that, when executed by the CPU, cause the CPU to automatically activate the electronic stethoscope in response to detecting a touch input via the sensor.

The utility model relates to a sputum excretion vest which comprises a vest body, a sputum excretion mechanism and a control host. The inner side of the vest body is covered with a plurality of air bags communicated with one another, one air bag is connected with an inflation connector, and the inflation connector is externally connected with inflation equipment; the sputum excretion mechanism comprises a front plate, a rear plate and two side plates, the front plate and the rear plate are detachably connected, the two side plates are connected to the two sides of the front plate and the two sides of the rear plate respectively, a plurality of ultrasonic probes are arranged on the inner sides of the front plate and the two side plates in an array mode respectively, and a plurality of vibration modules are arranged on the inner sides of the front plate, the rear plate and the two side plates in an array mode respectively. After a patient wears the vest body, the air bag is inflated through inflation equipment, the ultrasonic probes and the vibration modules are attached to the body of the patient, then the multiple ultrasonic probes are started to detect different parts of the lungs of the patient, and when the area where a certain ultrasonic probe is located detects secreta in the lungs, the vibration intensity of the vibration modules in the corresponding area is adjusted; the vibration sputum excretion treatment is more accurate, and the sputum excretion efficiency is higher.

The invention provides a multi-modal data fusion intelligent children asthma prediction system, which comprises a data acquisition and preprocessing unit, a feature extraction unit, a data fusion and intelligent prediction unit and a decision and feedback unit, and is characterized in that the data acquisition and preprocessing unit is used for acquiring multi-modal physiological signal data; the feature extraction unit is used for extracting features of the multi-modal physiological signals; the data fusion and intelligent identification unit is used for carrying out feature fusion on the extracted multi-modal physiological signal data; the decision and feedback unit is used for comprehensive judgment and dynamic feedback adjustment; in the dynamic feedback and self-adaptive adjustment part, a correction coefficient and the weight of the model are adjusted according to real-time data, so that the prediction probability is more accurate. The system effectively overcomes the problem that the existing system depends on a single data source or medical staff depends on subjective evaluation.

The present invention relates to a system and method for acquiring and analyzing physiological data from a user. The system includes a plurality of interconnected devices, which may communicate sensor data to a personal mobile electronic device. Each interconnected device includes at least one sensor to acquire physiological data. In addition, at least one sensor is operably connected to the body of the user. Further, the interconnected biometric devices may be implanted medical devices and / or wearable electronic devices. The personal mobile electronic device is wirelessly connected to each of the plurality of interconnected biometric devices. In addition, the personal mobile electronic device is configured to receive and analyze physiological data acquired by each of the plurality of interconnected devices and to compute the difference between the values of the same physiological parameter measured at a different location of the user's body.

The invention relates to the technical field of health monitoring, in particular to an electrocardiograph monitoring intelligent diagnosis system based on deep learning and multi-modal fusion technology, which is characterized in that a multi-modal synchronous sensing unit synchronously acquires ECG, PCG and PPG data streams with aligned timestamps, and an edge intelligent diagnosis unit operates a lightweight model with the parameter smaller than 1MB, so that the ECG monitoring intelligent diagnosis system is obtained. The cloud fusion analysis unit is used for dynamically fusing multi-source data by adopting a cross-modal attention mechanism to generate a ventricular fibrillation probability PVF, and the dynamic collaborative decision-making unit is used when R is equal to III or Cilt, and the cloud fusion analysis unit is used for dynamically fusing the multi-source data by adopting a cross-modal attention mechanism to generate a ventricular fibrillation probability PVF; deep diagnosis, PVFgt, is triggered within 5 seconds at 0.9; and when 0.95, a third-level audible and visual alarm is triggered, and the closed-loop optimization unit continuously optimizes the model by distilling and compressing the misdiagnosis sample. According to the intelligent diagnosis system for electrocardiograph monitoring, rapid edge diagnosis is achieved through a lightweight model, critical cases can respond rapidly through cloud cooperation, and the accuracy and timeliness of heart disease diagnosis are improved.

The invention discloses cross-scene reusable multi-modal physiological intelligent monitoring equipment and system. The equipment comprises a multi-modal physiological acquisition sensing array, an acquisition control module, a placement detection module, a self-adaptive parameter adjustment module and a signal processing module. The multi-modal physiological acquisition sensing array comprises an ambient light sensor, a photoelectric volume pulse wave sensor, a temperature sensor, a sensing electrode, a pressurizing electrode, a sound collector, an inertial measurement unit and the like; the placement detection module is used for calculating the comprehensive confidence coefficient of the current physiological electric signal belonging to each physiological signal type for each signal acquired by the multi-modal physiological acquisition sensing array, so as to judge the physiological signal type to which the physiological electric signal belongs; the adaptive parameter adjusting module is used for generating a sampling rate, an amplification factor, a filtering bandwidth and an ADC bit depth corresponding to the current physiological signal type; and the signal processing module is used for carrying out signal processing according to the parameters of the self-adaptive parameter adjusting module. Multi-modal physiological signal monitoring can be realized by adopting one set of equipment.

The invention provides a breath sound signal processing and evaluating method and system. The method comprises the following steps: firstly, collecting breath sound signals of a plurality of preset parts of the chest of a user through an electronic stethoscope, and collecting clinical symptom data through a user interface; a first feature set and a second feature set are then extracted from the breath sound signal, where the first feature set is extracted from six sites for preliminary analysis and the second feature set is extracted from four sites for in-depth analysis. And performing analysis based on the first feature set and the first machine learning model, and outputting a first indication value. Decision is made in combination with clinical symptom indication values, and deep analysis is started when any indication value reaches a threshold value. The deep analysis outputs a second indication value based on a second feature set and a second machine learning model. And finally, fusing a plurality of indication values to generate a comprehensive evaluation result.

The application discloses a diagnosis data processing method and device and a storage medium, relates to the technical field of data processing, and comprises the following steps: acquiring a heart-lung auscultation sound signal collected by a collection device and a traditional Chinese medicine (TCM) diagnosis standard description received based on an interactive interface; determining a western medicine quantitative diagnosis index according to the heart-lung auscultation sound signal, and correlating a TCM syndrome differentiation type according to the TCM diagnosis standard description and the western medicine quantitative diagnosis index; and generating and outputting a TCM medication recommendation scheme corresponding to a diagnosis result according to the western medicine quantitative diagnosis index and / or the TCM syndrome differentiation type. The application provides strong objective evidence and quantitative reference by differentiating the TCM syndrome differentiation type according to the auscultation sound. When diagnosing and treating diseases, the TCM highly depends on and integrates the auscultation result, and takes the auscultation result as one of key bases for differentiation, prescription selection, treatment effect evaluation and prognosis.

A health-device adapter for handheld electronic devices comprises a holder (20) with a base (22), longitudinally extending so as to define broad front wall (24) and back wall (26), and a gripping assembly (30, 32) to hold, in use, a handheld device, and connected to the base (22). The adapter further comprises a health-device support (50) connected to the holder (20) and suitable to support a health-device (100). The health-device support (50) is movably engaged to the gripping assembly of the holder (20).

The invention provides an active noise reduction stethoscope. The active noise reduction stethoscope comprises a sound receiving device, a sound processing device and a control unit, the sound receiving device is provided with a main microphone and an auxiliary microphone, the main microphone is configured to receive and output a first sound signal, and the auxiliary microphone is configured to receive and output a second sound signal. The sound processing device is coupled to the sound receiving device to receive the first sound signal and the second sound signal, and converts the first sound signal and the second sound signal into a third sound signal and a fourth sound signal in a digital mode respectively. The control unit is coupled to the sound processing device and calculates a correlation between the third sound signal and the fourth sound signal for determining whether to execute an active noise reduction operation.

The present invention provides a device (100) and method for measuring and / or monitoring auscultation and other biomedical signals of a body part of a subject, the device (100) comprising: a plurality of sonographic sensors sensitive to surface vibrations-PG sensors (110) configured to measure acoustic activity of the body part, and providing a PG signal (115) indicative of acoustic activity of the body part, the PG sensor (110) being adapted to be arranged on the body part to cover different independent auscultation areas of the body part. The device further comprises at least one other biomedical sensor-BIO sensor (120) configured to independently measure a respective activity of the body part according to the sensor type and to provide at least one BIO signal (125) indicative of the respective activity of the body part. The device further comprises a signal processing unit (160) configured to acquire a signal (115, 125) from each sensor (110, 120) and to collectively perform a signal source specific analysis by combining information of the PG signal (115) with corresponding arrangement information of the PG sensor (110) on the body part and information of the BIO signal (125).

The application belongs to the field of digital medicine, and particularly relates to a chronic obstructive pulmonary disease (COPD) differential system based on digital lung sound, a construction method and application thereof. The system comprises a data acquisition module, a preprocessing module, a conversion module, a data module, a data processing module, a data enhancement training module, a deep learning module, a differential module, a grading module and a display module. Digital lung sound records of candidates are collected, converted into mel spectrograms, and diagnosed by using a deep neural network. The candidates are COPD patients, healthy people, or people suffering from other lung diseases, and the disease grade of the COPD patients is given by a device carrying the differential system. The examination only needs to collect lung sounds in clinical or other places, reduces radiation damage and possible infection risk in the conventional diagnosis process, and provides a scheme for early diagnosis of COPD, and improves the diagnosis efficiency of the respiratory department, especially the COPD disease.

Methods and systems (100, 500) for monitoring heart valve operation using signals (812) detected by a heart sound sensor (270, 300, 525) includes memory (260, 522) to store program instructions and one or more processors (220, 520) that, when executing the program instructions receive seismocardiograpy (SCG) signals (812) detected by a heart sound sensor (270, 300, 525) along an axis. The SCG signals (812) include heart sound signals for a series of heartbeats over a first time period. The SCG signals (812) are segmented into SCG segments (826) 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 (826). At least a portion of a second subset of the SCG segments (826) 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 (826) having the matching scores that satisfy a matching threshold.

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 utility model relates to the technical field of medical instruments, and discloses an adjustable stethoscope which comprises an elastic plate, the two ends of the elastic plate are fixedly connected with mounting cylinders, the two mounting cylinders are arranged in a V shape, one end of each mounting cylinder is fixedly connected with a fixing block, a cavity is formed in each fixing block, and the fixing blocks are fixedly connected with the mounting cylinders. The inner side wall of the cavity is in sliding connection with a pressing rod. According to the adjustable stethoscope, through the arrangement of the mounting cylinder, the pressing rod, the spring, the connecting block, the clamping block and the ear hooks, when the distance between earplugs on the two ear hooks is adjusted, a pressing plate is firstly pressed to drive the pressing rod to move, meanwhile, the spring is compressed to drive the clamping block to move through the connecting block, the pressure of the clamping block on the ear hooks is reduced, and the clamping block is far away from the ear hooks; then the ear hooks can be pulled, under the action of the mounting cylinders, the relative distance between the earplugs on the two ear hooks is changed, the stethoscope can better adapt to the head of each user, and therefore the auscultation comfort level and the sound conduction effect are improved.

A processor of an ear-worn device such as an earbud may receive acceleration data from an accelerometer of the earbud. The processor may determine a position of a body wearing the earbud based on the acceleration data. The processor may determine a pathology of the body. The processor may generate, based on the position of the body and the pathology, a therapy recommendation. The processor may output an indication of the therapy recommendation via the earbud or one or more other devices.

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.

An ear-worn device may include an accelerometer and / or an electroencephalography (EEG) sensor to provide recommendations based on user activity and / or physiological responses. The ear-worn device may use the accelerometer to capture acceleration data to assess the wearer's movement rate, and detect changes. Using data from the EEG sensor, the processor may detect eyelid blinks and other signals of fatigue. The processor may generate a treatment recommendation based on any combination of the movement rate, changes, eyelid blinks, and / or signals of fatigue.

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.

A lung sound analysis system includes a storage means for storing therein the history of auscultation observations on lung sound data of each of auscultation positions of a subject who is a heart failure patient, a calculation means for calculating appearance frequency of abnormal sounds for each of the auscultation positions on the basis of the history, a determination means for determining the sequence of the auscultation positions at which lung sounds are to be auscultated from the subject, on the basis of the calculated appearance frequency, and an acquisition means for giving guidance on the auscultation positions of the subject to an operator according to the determined sequence, and acquiring time-series acoustic signals including lung sounds from a guided auscultation position. The system supports medical decision-making for healthcare professionals monitoring heart failure patients.

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.

Disclosed is a system for monitoring a heart condition in a companion animal. The system can collect electrocardiogram data of a companion animal through a capacitive electrocardiogram sensor without prior preparation such as hair removal, collect phonocardiogram data and ballistocardiogram data through a phonocardiogram sensor and a ballistocardiogram sensor, and generate cardiogram-integrated data available for diagnosing a heart condition in the companion animal by merging the electrocardiogram data, the phonocardiogram data, and the ballistocardiogram data.

A lung sound analysis system includes a storage means for storing therein the history of auscultation observations on lung sound data of each of auscultation positions of a subject who is a heart failure patient, a calculation means for calculating appearance frequency of abnormal sounds for each of the auscultation positions on the basis of the history, a determination means for determining the sequence of the auscultation positions at which lung sounds are to be auscultated from the subject, on the basis of the calculated appearance frequency, and an acquisition means for giving guidance on the auscultation positions of the subject to an operator according to the determined sequence, and acquiring time-series acoustic signals including lung sounds from a guided auscultation position. The system supports medical decision-making for healthcare professionals monitoring heart failure patients.