42 results about "Heart sounds" patented technology

Techniques and apparatuses are described for performing internal-noise-source filtering for active acoustic sensing. With internal-noise-source filtering (202), interference caused by a hearable (102) performing other operations (e.g., rendering audio content (204), performing active noise cancellation (206), or operating in accordance with a transparency mode (214)) can be attenuated within a received ultrasound signal to improve sensitivity and accuracy for audioplethysmography. This performance improvement enables audioplethysmography to be performed while the hearable (102) performs these other operations. Furthermore, it improves the ability of audioplethysmography to be used for voice processing, which can include voice activity detection, speech recognition, and / or conversation detection.

The invention provides a mitral valve regurgitation severity assessment method based on PCG signals, which comprises the following steps: firstly, synchronously acquiring signals from a heart sound area and an environmental background by adopting a dual-channel audio acquisition technology, eliminating environmental noise and operation interference (such as clothes friction and non-uniform pressing force of a stethoscope) through a self-adaptive noise elimination algorithm, and calculating the severity of the mitral valve regurgitation severity; and carrying out segmentation processing on continuous heart sound signals of the de-noised heart sound by adopting a dynamic time window segmentation strategy to generate short-time heart sound fragments, evaluating the quality of the short-time heart sound fragments in real time by using a model based on a lightweight convolutional neural network, and filtering low-quality fragments. Then, nonlinear features are directly extracted from the short-time heart sound fragments through a deep neural network model based on a clique block, after each effective fragment is subjected to four classifications, classification results of all the fragments are integrated through a majority voting mechanism, and final mitral valve regurgitation severity judgment is generated. The classification robustness is effectively improved through multi-fragment information fusion, and the random error of single-fragment analysis is reduced.

A wearable heart sound detection device includes an acoustic sensing device for collecting heart sound signals of user's body. A wireless transmission device is connected to transmit and receive data, an e-SIM embedded in the wearable heart sound detection device, wherein said acoustic sensing device includes a capacitive sound sensor, a piezoelectric sound sensor or the combination thereof. A circuit assembly electrically is connected with the capacitive sound sensor or said piezoelectric sound sensor, wherein the capacitive sound sensor and the piezoelectric sound sensor are integrated on a flexible substrate. The e-SIM is compatible with a wireless transmission protocol selected from a group of WiFi, 4G, 5G, 6G or any combination thereof.

Systems and methods for recognizing and tracking heart sound components are disclosed. An exemplary medical-device system includes a data receiver to receive heart sound information, and a heart sound recognition circuit to generate a representative heart sound segment such as an ensemble average of a selected portion of heart sound segments. The heart sound recognition circuit recognizes a heart sound component from the representative heart sound segment using a time-based tracking algorithm, and evaluates a performance index of the time-based heart sound tracking algorithm. Based on the performance index, a decision can be made whether or not to switch to a spectral-based tracking algorithm to recognize the heart sound component from the representative heart sound segment. A physiologic event detector can detect a cardiac event using the recognized heart sound component.

Systems and methods are disclosed to an ambulatory medical device. The ambulatory medical device comprising an activity sensor configured to produce an activity signal representative of activity level of a patient, a heart sound sensor configured to produce a heart sound signal, and a control circuit. The control circuit is configured to detect a change in activity level of the patient using the activity signal, enable monitoring of the heart sound signal in response to detecting the change in activity level, measure intensity of an S3 heart sound in the heart sound signal, and produce an indication of pulmonary hypertension (PH) when the measured intensity of the S3 heart sound exceeds a PH detection threshold intensity.

The invention discloses a physiological sound separation and extraction method, and relates to the technical field of body sound signal processing and analysis. Comprising the steps of collecting human body physiological sound signals, constructing a multi-level signal separation model based on statistical characteristics, sequentially realizing sound signal elimination and extraction of various physiological sound signals, establishing a multi-dimensional characteristic parameter set, and performing intelligent identification and classification on the extracted physiological sound signals in combination with a deep learning classifier. And carrying out physiological and pathological state evaluation according to an identification and classification result. By comprehensively applying various signal processing and classification algorithms, automatic separation and accurate classification of heart sound, lung sound and borborygmus signals are realized, the physiological sound signal processing efficiency and precision are remarkably improved, and clinical doctors can be helped to quickly judge diseased regions and diseased property.

An apparatus for quick detection of a blood pressure level is described. The apparatus comprises a module, a device and a network processor. The module is for capturing data of a heart sound. The device is for transforming the data into a plurality of time-frequency spectrograms having a plurality of image features. The network processor is trained to analyze the image features of the time-frequency spectrograms, for giving the blood pressure level. The network processor is equipped with a convolutional neural network trained to analyze the image features of the time-frequency spectrograms, by focusing on a second heart sound, for detecting the hypertension. The network processor is also trained to identify the time-frequency spectrograms within a frequency band, to distinguish the long-term hypertension and the exercise-induced transient hypertension.

The invention discloses a heart sound signal acquisition and automatic classification method based on deep learning, and aims to solve the problems that wireless transmission time delay of heart sound signals is high, weak pathological features are difficult to extract and classification accuracy is low. The method comprises the following steps: acquiring signals of four auscultation areas by using a high-fidelity wireless digital stethoscope; high-speed and low-delay transmission of medical-level signals is realized by utilizing a star flash transmission module and combining predictive coding and Huffman coding compression mechanisms; in the preprocessing stage, self-adaptive wavelet denoising based on signal energy entropy is adopted, a multi-resolution fusion feature extraction strategy is provided, the problem of time-frequency resolution tradeoff is solved through narrow window and wide window dual-channel parallel processing, and transient positioning features and pathological frequency domain textures are captured at the same time; the classification model adopts a multi-scale parallel convolution structure and is matched with a recurrent neural network and an attention mechanism, so that the problem that transient and continuous heart sound features are difficult to consider at the same time in a single scale is solved. According to the method, the accuracy and robustness of heart sound classification are remarkably improved.

The present application discloses a self-powered portable multiple vital signs monitoring device and method for personnel in mines, wherein the device comprises: a monitoring vest for monitoring the heart sound data of personnel; a monitoring bracelet for monitoring the blood oxygen data, heart rate data and body temperature data of personnel. The present application can monitor multiple vital signs of personnel and effectively ensure the life safety of personnel.

Systems and methods for recognizing and tracking heart sound components are disclosed. An exemplary medical-device system includes a data receiver to receive heart sound information, and a heart sound recognition circuit to generate a representative heart sound segment such as an ensemble average of a selected portion of heart sound segments. The heart sound recognition circuit recognizes a heart sound component from the representative heart sound segment using a time-based tracking algorithm, and evaluates a performance index of the time-based heart sound tracking algorithm. Based on the performance index, a decision can be made whether or not to switch to a spectral-based tracking algorithm to recognize the heart sound component from the representative heart sound segment. A physiologic event detector can detect a cardiac event using the recognized heart sound component.

The application discloses a flexible wearable electronic heart sound stethoscope, which comprises a sampler, a preamplifier, an anti-aliasing filter and an analog-to-digital conversion module which are sequentially connected and form a whole, and the sampler is packaged by a flexible material, wherein two silicon microphones are used to collect heart sounds and noise respectively, and the silicon microphone comprises a MEMS analog acoustic sensor which is installed on an FPCB. In the application, the sampler adopts the FPCB circuit board, so that the comfort and the closed sound cavity are achieved, and the subsequent signal processing part adopts the hard PCB circuit board, so that the high integration and the stable signal processing are achieved; the two circuit boards are connected through an interface, so that the sampler is easy to replace, meanwhile, the FPCB part with a relatively low service life can be replaced in time, and the use cost of the flexible electronic heart sound stethoscope is reduced. In addition, through reasonable acoustic structure design, the signal received by the MEMS analog acoustic sensor is relatively large.

A wearable heart sound detection system, which includes an acoustic sensing device for collecting heart sound signals of the body, performing signal amplification, filtering, digitization and other preprocessing on the collected heart sound signals, and outputting the preprocessed heart sound signals; a computing electronic device communicatively coupled to the acoustic sensing device for acquiring the preprocessed heart sound signal, and a cloud data database communicatively coupled to the external computing electronic device. The acoustic device includes a capacitive sound sensor, a piezoelectric sound sensor and a circuit assembly. The circuit assembly is respectively electrically connected with the capacitive sound sensor and the piezoelectric sound sensor. The circuit assembly, the capacitive sound sensor and the piezoelectric sound sensor are integrated on a flexible substrate.

The invention belongs to the technical field of medical instruments, and particularly relates to a head-mounted stethoscope for heart disease monitoring, which is used for daily physical examination work and application to a heart rehabilitation path after a cardiac pacemaker is implanted, and comprises a U-shaped frame, and a U-shaped groove is formed in the U-shaped frame. By arranging the mounting rings and the sound boxes in the earmuffs, when the device is used, the earmuffs can be worn on the ears, the sound receiving heads are placed in a manner of being attached to the thoracic cavity of a patient, the sound receiving heads are used for receiving heart sounds of the patient, heart sound data are transmitted to the control host through the wires, and then the control host plays the heart sounds through the sound boxes; the U-shaped frame is arranged on the U-shaped frame, damage to external auditory canal mucous membranes caused by long-time auscultation work is effectively reduced, meanwhile, the holographic glasses are arranged to be connected with the U-shaped frame, the holographic glasses are connected with the control host, and therefore heart sound data are converted into beating images through the control host, visualization is achieved, and the auscultation effect and the auscultation efficiency are effectively improved.

The invention discloses a preparation method of a triboelectric acoustic sensor. The preparation method comprises the following steps: washing animal leather with ethanol and deionized water respectively, and drying the animal leather; then PVDF is mixed with nanoparticles and then dissolved in an organic solvent, stirring is conducted for 4-12 h at the temperature of 40-70 DEG C till complete dissolution, and the obtained solution is subjected to electrostatic spinning to obtain a highly-polarized composite film; the dried animal leather is used as a positive friction layer of the sensor, a conductive material is used for plating a conductive layer on the surface, the obtained electrostatic spinning film is used as a negative friction layer, the friction layers are adhered through conductive glue, and the triboelectric acoustic sensor is obtained. The sensor is suitable for detecting heart sound, lung sound, intestine sound and other internal sound signals of the human body, can convert the picked weak sound signals into electric signals, and has guiding significance in early diagnosis of heart diseases, pneumonia and other diseases in the fields of biomedicine and the like.

The invention relates to the technical field of medical signal processing, and discloses an intelligent auscultation data optimization method and system based on big data, and the method comprises the steps: obtaining auscultation signal data, respectively extracting an energy feature sequence, a complexity feature sequence and a frequency feature sequence, and constructing a three-dimensional feature matrix; calculating local density distribution of each feature point in the three-dimensional feature matrix, and performing density segmentation on the three-dimensional feature matrix to form a manifold skeleton; projecting feature points in the three-dimensional feature matrix to a manifold skeleton, preliminarily separating heart sound signals and lung sound signals, and performing secondary projection on the preliminarily separated lung sound signals to obtain optimized lung sound signals; and calculating geodesic distances of adjacent sampling points on the surface of the manifold skeleton based on the preliminarily separated heart sound signals, identifying a signal damage area, calculating a shortest geodesic path, and mapping the shortest geodesic path back to a time domain for restoration to obtain optimized heart sound signals. And the physiological authenticity and the use benefit of auscultation data are effectively improved.

The invention belongs to the technical field of biomedical signal processing, and particularly relates to an automatic respiratory rate calculation method for respiratory sound processing of an electronic stethoscope, which comprises the following steps of: S1, acquiring a period of respiratory sound signal with the duration of Ts (Ts is greater than or equal to 15 seconds) and the sampling rate of fs by using the electronic stethoscope; s2, performing short-time Fourier transform on the obtained breath sound signal vector x; s3, calculating the amplitude S of the time-frequency spectrum matrix S; s4, carrying out homomorphic envelope st on the time domain vector # imgabs0 # of the intermediate frequency component; and S5, searching all peak values greater than a preset threshold Eth on the time domain homomorphic envelope st of the intermediate frequency component. By calculating the time domain homomorphic envelope of the intermediate frequency component, main interference components including heart sound can be automatically eliminated, the detection specificity of the breath sound signal component is greatly improved, and then the calculation precision of the breath rate is improved; respiratory cycle detection and respiratory rate calculation can be realized only by searching a peak value meeting a threshold condition on the time domain homomorphic envelope of the intermediate frequency component without any pattern recognition method with relatively high complexity.

The invention belongs to the field of stethoscopes, and particularly relates to a visual stethoscope based on an electronic heart sound integration technology, which comprises a visual stethoscope main body, a sensor assembly, a signal conditioning circuit, a microcontroller, a display module and an algorithm module, the signal conditioning circuit comprises a pre-amplifier, a filter and an A / D converter, the algorithm module comprises a signal processing algorithm, a waveform display algorithm and a user interaction interface, and the signal processing algorithm comprises a filtering algorithm, a noise reduction algorithm and a feature extraction algorithm. An electronic heart sound integration technology, an electret type sound sensor or an MEMS microphone is adopted, so that the capacity of collecting weak heart sound signals is improved; the quality of heart sound signals is improved through a signal processing algorithm. A user interaction interface is matched to improve the practicability of the equipment; and the tightening degree of the diaphragm can be automatically and dynamically adjusted, and the usability is good.

An example system includes an implantable medical device includes a heart sound sensor configured to sense heart sound signals; and an optical sensor configured to sense optical signals; and processing circuitry configured to: determine blood pressure values of a patient based on the optical signals; determine heart sound data using the heart sound signals; determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

The utility model relates to an intelligent stethoscope with improved efficiency, which comprises a stethoscope head, a sound tube and ear tubes, an observation component is arranged between the stethoscope head and the sound tube, a control unit, a heart sound sensor and a plurality of miniature volume sensors are arranged in the stethoscope head, the control unit is respectively connected with the heart sound sensor and the miniature volume sensors, and the ear tubes are connected with the control unit. The multiple miniature volume sensors are evenly arranged on the periphery of the stethoscope head, multiple LED indicator lamps are arranged on the edge of the back face of the stethoscope head in a surrounding mode, the multiple miniature volume sensors and the multiple LED indicator lamps are correspondingly connected, and a monitoring display screen for displaying the heart rate is arranged on the observation assembly. And the monitoring display screen is electrically connected with the heart sound sensor through the control unit. The system can give an abnormal alarm when a preset threshold value is reached so as to help a doctor to quickly diagnose an illness state; and the volume intensity position is fed back in real time through the visual LED indicator lamp, so that a doctor can be helped to quickly position an auscultation point.

Provided are an information processing method and the like capable of supporting determination of a state of a heart on the basis of feature amounts calculated from an electrocardiogram, a heart sound waveform, or a pulse wave waveform. According to the present invention, a computer acquires an electrocardiogram, a heart sound waveform, and a pulse waveform of a patient. The computer acquires: a first feature amount by a first formula using a I sound position or a II sound position obtained from the heart sound waveform, a Q wave position obtained from the electrocardiogram, and a notch position and a pulse wave rising position obtained from the pulse wave waveform; a second feature amount by a second formula using a maximum amplitude of the I sound or an area obtained by time-integrating a waveform of the I sound, or the maximum amplitude of the II sound or the area obtained by time-integrating a waveform of the II sound; and a third feature amount by a third expression using an element feature amount obtained by using the pulse wave waveform. In a case in which the first feature amount to the third feature amount are input, the computer inputs the first feature amount to the third feature amount thus acquired to a trained model trained so as to output an estimated value of an intracardiac pressure of the patient, thereby acquiring the estimated value of the intracardiac pressure of the patient.

A method and device for detecting phrenic nerve stimulation (PNS) in, or using, a cardiac medical device. A test signal sensitive to contraction of a diaphragm of a patient may be sensed and signal artifacts of the test signal within each of a first window of the test signal prior to a predetermined cardiac signal and a second window of the test signal subsequent to the predetermined cardiac signal may be determined. The PNS beat criteria may be evaluated, for example, using the test signal, which may be a heart sounds signal.

The present disclosure provides a human feature acquisition device, monitoring device, system, method, equipment, and storage medium. The human feature acquisition device adopts non-metallic materials and is characterized in that it includes: an optical fiber for receiving light and transmitting it; at least one displacement conversion structure, which is respectively connected to the optical fiber and is used to convert the vertical displacement of the human body surface into a change in the bending radius of at least one optical fiber; the light has a preset power encoding, and the position of the at least one displacement conversion structure is confirmed by monitoring the power change caused by the change in the bending radius of the optical fiber at the at least one displacement conversion structure, so as to realize the collection of human feature data. The present disclosure can realize the simultaneous monitoring of human feature data information such as the heart (such as heart rate, heart sounds), lung breathing, and even whether the patient is moving, etc. at multiple locations through light, and can be applied to magnetic resonance scenarios.

The flexible wearable electronic heart sound stethoscope comprises a sampler, a preamplifier, an anti-aliasing filter and an analog-to-digital conversion module which are sequentially connected, the sampler is packaged through a flexible material, two silicon microphones are used for collecting heart sound and noise respectively, and each silicon microphone comprises an MEMS simulation acoustic sensor installed on an FPCB. According to the invention, the sampler adopts the FPCB circuit board, so that the purposes of comfort and keeping the sound cavity closed are achieved, and the subsequent signal processing part adopts the hard PCB circuit board, so that the requirements of high integration level and stable signal processing are met; according to the flexible electronic heart sound stethoscope, the two circuit boards are connected through the interface, so that the sampler is easy to replace, meanwhile, the FPCB part with the short service life can be replaced in time, and the use cost of the flexible electronic heart sound stethoscope is reduced. In addition, through reasonable acoustic structure design, signals received by the MEMS simulation acoustic sensor are large.

This electronic device comprises a signal processing unit that detects vibration in a monitoring target on the basis of a transmitted signal that is transmitted as a transmitted wave and a received signal that is received as a reflected wave generated by reflection of the transmitted wave by the monitoring target. The signal processing unit generates first information relating to the heart sound of the monitoring target on the basis of vibration in a first portion of the monitoring target, and, in cases in which the first information does not meet a prescribed criterion, generates second information relating to the pulse wave of the monitoring target on the basis of vibration in a second portion different from the first portion of the monitoring target.

The invention provides heart sound and breath sound data processing software and method based on an artificial intelligence algorithm, and relates to the technical field of medical data processing. The system can be in Bluetooth connection with an electronic stethoscope to receive data and upload the data to a cloud server, supports user information input, realizes historical record management, and can upgrade a terminal algorithm. During auscultation of a doctor, heart sound and breath audio is automatically transmitted to the mobile terminal, heart noise and dry and wet rale are automatically recognized through an artificial intelligence algorithm, an analysis report is provided, and primary medical treatment can play a greater role in child health care, disease screening and preliminary diagnosis. The problem that efficient heart sound and breath sound data processing software combined with an artificial intelligence algorithm is lacked in the current market, especially application in grading diagnosis and treatment and primary medical treatment is solved.

The invention discloses a wearable physiological signal sensing system which comprises a system circuit board provided with an upper surface and a lower surface, a stethoscope arranged on the lower surface and used for sensing heart sound signals of a user, a plurality of electrocardio electrodes arranged on the lower surface and adjacent to the stethoscope and used for sensing electrocardio signals of the user, and an oximeter arranged on the upper surface and used for detecting the heart sound signals of the user. The blood oxygen sensor is used for sensing blood oxygen concentration and pulse wave signals of a user. The system circuit board is electrically connected with the stethoscope, the plurality of electrocardio electrodes and the oximeter, the system circuit board obtains the pulse wave transmission time of the user by comparing the electrocardio signal with the pulse wave signal or comparing the heart sound signal with the pulse wave signal, and the continuous blood pressure of the user is calculated according to the pulse wave transmission time.

The invention provides a lung sound signal acquisition method, electronic equipment and a storage medium, and relates to the technical field of signal processing.The method comprises the steps that when an electronic stethoscope is used for acquiring a heart and lung mixed sound signal, a pulse signal acquisition device is used for acquiring a pulse signal so as to acquire the heart and lung mixed sound signal and the pulse signal; inputting the pulse signal into a target electrocardiosignal prediction model to obtain an electrocardiosignal output by the target electrocardiosignal prediction model; performing time sequence alignment on the heart and lung mixed sound signal, the pulse signal and the electrocardiosignal; inputting the cardiopulmonary mixed sound signal, the pulse signal and the electrocardiosignal after time sequence alignment into a target lung sound signal prediction model to obtain a lung sound signal output by the target lung sound signal prediction model; according to the method, a plurality of complete heartbeat cycles do not need to be collected in advance, a heart sound template does not need to be created, low-delay real-time processing is supported, scenes such as arrhythmia can be effectively handled, and the quality of the obtained lung sound signals is remarkably improved.

The present invention discloses a multi-channel BCG signal detection and analysis method, comprising the following steps: (1) collecting at least four juxtaposed BCG signals; (2) selecting one of the optimal signal data for processing as the basis for subsequent heart rate detection. The present invention also provides corresponding heart rate detection methods and devices respectively. Among them, the heart rate detection method comprises the following steps: (a) performing EMD decomposition on the optimal signal to obtain a plurality of intrinsic mode components; (b) obtaining the spectrum of each independent component; (c) calculating the signal power ratio of 1-10 Hz based on the spectrum; (d) taking the signal greater than the signal power ratio threshold as the starting point, and simultaneously taking the amplitude value corresponding to the starting point as the threshold; (e) collecting the starting point positions of S1 and S2; (f) determining S1; (g) obtaining heart rate data based on S1. The present invention has well improved and enhanced the acquisition and calculation accuracy of heart sound and heart rate, providing an effective means and guarantee for non-contact and long-term heart sound signal monitoring.

A system and method for heart sound detection utilizes a radar sensor, wherein phase-tracking-based motion cancellation is performed to mitigate effects of body motions. Method steps include: receiving a radar return signal reflected from a chest region of the subject; down-converting the received radar return signal to a baseband signal; processing the baseband signal by performing phase-tracking-based motion cancellation to mitigate effects of body motions of the subject and produce a motion-cancelled signal; and processing the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject. Such steps may be performed using a radar transceiver and a signal processor coupled thereto.

The invention provides an intracardiac sound signal identification and classification method based on a deep learning architecture, and the method comprises the steps: obtaining an original intracardiac sound signal, and carrying out the filtering and noise reduction of the signal; performing short-time Fourier transform on single heartbeat signal data, and converting the single heartbeat signal data into a spectrogram of heartbeat signals; constructing a cyclic convolution residual network model, inputting the training set into the model for training, performing short-time Fourier transform on heartbeat signals in a test set needing to be identified, and generating a spectrogram required by the network model; the spectrogram of the heartbeat signals is input into the trained cyclic convolution residual network model, the model analyzes the spectrogram, and an atrial fibrillation or ventricular fibrillation recognition result is output; and executing the step on all data in the test set to obtain an atrial fibrillation or ventricular fibrillation identification result of each heartbeat signal.