24 results about "Breath sound" patented technology

The invention discloses a child abnormal breathing early warning and remote cooperation system for a family environment, and relates to the technical field of breathing data processing, and the child abnormal breathing early warning and remote cooperation system comprises a data acquisition module, a first data calculation module, a first data judgment module, a second data judgment module, a second data calculation module, a third data judgment module and a data output module. The system sequentially performs primary risk assessment, hidden risk mining and potential decompensation index calculation on the basis of a multi-modal signal processing technology by synchronously acquiring a breathing sound signal, a non-contact thoracoabdominal movement signal and a contact thoracoabdominal movement signal, so that graded early warning is realized, and a remote cooperation mechanism is automatically triggered according to the early warning level. According to the method, the hidden risk in the early stage of respiratory compensation of children can be effectively identified, and advanced early warning and timely intervention of abnormal respiration can be realized in a family environment.

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 present application relates to the technical field of signal processing, in particular to a health condition evaluation method based on wavelet packet decomposition, comprising: constructing a classification and recognition network model; recording patient breathing sound audio, and preprocessing the breathing sound audio to obtain an audio sequence; extracting features of the audio sequence through a wavelet packet decomposition algorithm to obtain a feature vector; inputting the feature vector into the classification and recognition network model for training to obtain a state change; and assigning scores after comparing the state change with a health state to obtain an evaluation result, wherein the present application evaluates the health condition through the constructed classification and recognition network model based on sound signals, improves the accuracy of the examination result, and solves the problem of low accuracy of the examination result of the existing evaluation method.

This invention discloses a multimodal respiratory disease-related data acquisition system and method. The data acquisition system includes a disposable oral-nasal breathing interface, an oral-nasal breath sound acquisition module, a bidirectional flow / differential pressure sensing module, a temperature and humidity auxiliary acquisition module, a chest and back multi-channel auscultation module, a channel auscultation front end, a trimodal respiratory data acquisition box, a mobile terminal annotation module, a clinical label association module, and a local storage module. The data acquisition method is as follows: Step 1, prepare the oral-nasal breathing interface and acoustic coupling patch; Step 2, start the trimodal respiratory data acquisition box; Step 3, the patient performs quiet breathing; Step 4, the patient performs a maximal inhalation followed by a forceful exhalation; Step 5, associate the clinical examination results with the acquired data; Step 6, output risk scores, abnormality prompts, or classification prompts. Beneficial effects: Reduces wind noise interference, allows acoustic signals and lung function data to be acquired at the same time reference, and reduces frictional noise and poor contact.

Monitoring module (6) for arrangement on a face mask, wherein the face mask is designed as a face half mask and has one or more paper layers and / or one or more fabric layers for covering a mouth and nose area of ​​a user and the monitoring module (6) is mechanically coupleable with the face mask and comprises the following: - an electronic circuit (61) for monitoring the use of the mouth and nose protection, - at least one first sensor element (681) arranged and configured on the face mask to provide a sensor signal suitable for detecting the breathing processes of a user, wherein the at least one first sensor element (681) - is designed and configured as a temperature sensor element to provide a sensor signal that is representative of the temperature of air located between a facial surface in the mouth and nose area of ​​the user and the mouth and nose protection, or - is designed and configured as a humidity sensor element to provide a sensor signal that is representative of the humidity of the air located between a facial surface in the mouth and nose area of ​​the user and the mouth and nose protection, or - is designed and configured as a pressure sensor element to provide a sensor signal that is representative of the air pressure of the air located between a facial surface in the mouth and nose area of ​​the user and the mouth and nose protection, or - is designed and configured as a carbon dioxide gas sensor element to provide a sensor signal representative of a carbon dioxide concentration in the air located between a facial surface in the mouth and nose area of ​​the user and the protective element of the mouth and nose protection, or - is designed and configured as a sound transducer sensor element to provide a sensor signal that is representative of a breath sound and / or exhalation sound of the user of the mouth and nose protection device or - is designed and configured as a thermal conductivity sensor element to provide a sensor signal representative of the thermal conductivity of the air located between a facial surface in the mouth and nose area of ​​the user and the protective element of the mouth and nose covering, and - a power supply unit (64) for at least the electronic circuit (61), wherein the power supply unit (64) comprises a solid-state accumulator without electrolytes.

The invention discloses a children breath sound multi-level classification system based on AST, and the system comprises a data collection and preprocessing module which collects and preprocesses children breath sound data, and obtains children breath sound features; the coarse classification module is used for extracting global features based on a pre-trained AST encoder and outputting a coarse classification result; the fine-grained feature detection module is used for obtaining fine-grained features by utilizing AttnPool1d which can be learned and scaled based on the global features, outputting logit values of specific pathological features through MLP, executing a hierarchical gating mechanism according to a coarse classification result, and keeping two levels of logic consistent through balanced-medal Focus Loss and hierarchical consistency constraint; and the diagnosis output module is used for comparing the preset threshold value of the specific pathological feature with the logit value and outputting a result indicating whether the specific pathological feature exists or not. The children breath sound multi-level classification precision is effectively improved.

The invention discloses an audio deep forgery detection method and device, equipment, a storage medium and a product, and the method comprises the steps: carrying out the detection of to-be-processed audio data through a pre-trained audio deep forgery detection model, and carrying out the detection of the to-be-processed audio data in the detection process of the audio deep forgery detection model, extracting audio frequency features and audio time features of the audio data based on a time-frequency double-branch structure, and enhancing breathing sound clues in the audio time features through a breathing sound feature linear modulation mechanism; performing cross attention fusion on the audio time features and the audio frequency features, and predicting the fused features to obtain a forged audio detection result; according to the method, the breathing sound feature linear modulation mechanism and the cross attention fusion mechanism are utilized, breathing sound clue enhancement and fusion of the breathing sound clues and the time-frequency features can be achieved, natural voice fine clues such as breathing sound are effectively recognized, and therefore the accuracy of audio deep forgery detection is improved.

1. The name of the design product: breathing sound host. 2. The use of the design product: the design product is a breathing sound collecting device, which can realize the collection of breathing sound. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention provides an intelligent early warning intervention system for chronic obstructive pulmonary diseases, and relates to the technical field of medical instruments. Comprising a core host, the core host is integrated with an active multi-modal sensor arm and an integrated drug delivery arm, a patient can be actively guided to enter a standardized test posture, and high-quality multi-modal physiological data such as lung function, blood oxygen and breath sound are synchronously collected; an intelligent medicine bin and an automatic delivery mechanism are arranged in the intelligent medicine box, and accurate taking, delivery and auxiliary inhalation of medicine can be completed after early warning is triggered. The standardized seat assists in fixing the sitting posture of a patient through a physical guiding structure, and the wearable monitoring vest achieves respiratory rhythm monitoring and auxiliary sputum excretion. According to the system, all the modules are cooperated through the central controller, and a complete entity closed loop from standard monitoring to intelligent early warning to automatic intervention is constructed. The system effectively solves the problems that household lung function monitoring data is unreliable and intervention is difficult to execute after early warning, and the timeliness, safety and patient compliance of chronic disease management are remarkably improved.

The application discloses a kind of asthma detection method and portable asthma detection equipment, the method includes obtaining user breathing sound;Segmentation after de-noising, and into frequency spectrum by short-time Fourier transform and logarithmic transformation conversion;And frequency spectrum is input to neural network model to obtain wheezing and wheeze sound.Detection equipment includes shell, main control unit is built in shell, bottom is embedded with contact microphone connected with main control unit;Wherein, main control unit is used to control contact microphone to collect breathing sound and utilize the asthma detection method described above to identify wheezing and wheeze sound.The detection method of the application is fast in operation, good in real-time, can realize the timely detection to the asthma condition of patient, and has good distinguishing effect;While detection equipment is portable, simple to operate, easy to carry.

The invention discloses a tuberculosis auxiliary diagnosis system fusing chest radiography and breath sound characteristics. The tuberculosis auxiliary diagnosis system comprises a breath sound acquisition module, a breath sound dynamic analysis module, a chest radiography acquisition module, a chest radiography detection module, an activity discrimination fusion module and a result output module. Breathing sound signals are collected at a plurality of standard point positions on the body surface of a patient, dynamic abnormal indexes are extracted, meanwhile, chest radiography images are obtained, focus attribute information is recognized, an activity judgment fusion module conducts consistency analysis on the chest radiography images and the dynamic abnormal indexes, standardized risk scores are generated, and high-risk, low-risk or uncertain grading results are output. According to the method, the misdiagnosis and missed diagnosis risks can be effectively reduced under the complex conditions of old focus combined activities, early hidden cases, combined chronic obstructive pulmonary disease and the like, and a visual diagnosis report containing a chest radiograph thermodynamic diagram and a breath sound frequency spectrum is provided. Compared with single chest radiography or single breath sound analysis, the sensitivity and specificity of active tuberculosis diagnosis are improved, and the method has high clinical application value.

The invention belongs to the field of aviation technology design, and particularly relates to a pilot fast-wearing type oxygen mask microphone communication noise reduction method and a system thereof.The pilot fast-wearing type oxygen mask microphone communication noise reduction method comprises the steps of sound source monitoring, specifically, sound in a closed cavity of a fast-wearing type oxygen mask is continuously collected through a microphone; a voice processing step: judging whether the sound in the closed cavity of the quick-wearing oxygen mask is breathing sound or not in real time; the amplitude, the frequency, the period and the unvoiced and voiced sound distribution characteristics are synthesized to judge whether the sound in the closed cavity of the fast-wearing oxygen mask is breath sound or not; a logic control step: when the sound in the closed cavity of the quick-wearing oxygen mask is judged to be breathing sound, switching off microphone communication; and when the sound in the closed cavity of the quick-wearing oxygen mask is judged not to be breathing sound, microphone communication is turned on.

The application discloses a myopia prevention and control red light irradiation system and method based on breath synchronization, and belongs to the technical field of vision care. The system comprises an intelligent terminal, a light-emitting plate and an optical projection module. The light-emitting plate is provided with a red light source with a wavelength range of 600-1000 nm, a miniature camera with a microphone, a single-chip microcomputer and a communication module, and the camera is arranged at a position corresponding to a physiological blind spot of an eyeball to realize non-invasive monitoring. The intelligent terminal completes double user identification through an eye image, generates a guide signal through breath sound analysis, and controls the light-emitting plate to output periodically changing red light at a frequency of 6-12 times per minute. The application realizes a light-pupil-breath triple synchronization closed loop through two core mechanisms of pupil pumping effect and light-oxygen double driving: the former utilizes periodically changing light intensity to drive the pupil to rhythmically dilate and contract, and mechanically enhances choroidal blood flow; and the latter uses light intensity change to guide the user to slowly and deeply breathe, and realizes light-oxygen synergistic effect of mitochondrial metabolism. The application provides two modularization schemes of low-cost universal and enhanced digital display, is provided with multiple safety protection mechanisms, and significantly improves the myopia prevention and control effect and the use compliance of children.

The application discloses a lung function evaluation method and system based on bidirectional cross attention. The breath sound of a subject is collected, and the original audio is preprocessed to obtain standardized audio frames. Multi-scale time-frequency feature extraction is performed on the standardized audio frames to obtain a breath sound time-frequency feature sequence. The time-frequency feature sequence and a learnable hidden vector are input into a BiXT encoder for bidirectional cross attention encoding to obtain a breath sound time sequence position feature and a pathological semantic global feature, respectively. Spatial attention and channel attention enhancement of position information and semantic information are performed through a double attention module to realize feature fusion. Frame-level abnormal event detection, sequence-level breath sound classification and lung function state evaluation are performed based on the fused features. Model training is completed. The application considers global context modeling, feature fine extraction and multi-task joint output, which helps to improve the accuracy of breath abnormality detection, breath sound classification and lung function evaluation, and enhances the result interpretability.

Extract intracorporeal sounds (such as low-frequency-range heart sounds or breath sounds) by not only reducing steady noise (such as low-frequency-range environmental sounds or noise) but also reducing unexpected noise (such as high-frequency-range cries or speaking voices). This keeps up the intracorporeal sounds (such as low-frequency-range heart sounds or breath sounds), while reducing the unexpected noise (such as high-frequency-range cries or speaking voices), using an improved adaptive filter unit and an improved nonlinear filter unit. Here, a reduction level of the unexpected noise (such as high-frequency-range cries or speaking voices) and a level of keeping up the intracorporeal sounds (such as low-frequency-range heart sounds or breath sounds) are quantitatively calculated.

The invention discloses a high-sensitivity piezoelectric transducer for collecting physiological signals of a human body. The high-sensitivity piezoelectric transducer comprises an acoustic cavity, a cavity vibrating diaphragm and a piezoelectric transduction element, the piezoelectric transduction element is composed of a piezoelectric fiber, a sensing diaphragm and an electric conductor structure; the piezoelectric fiber is composed of at least one section of long-strip-shaped flexible piezoelectric film strip, the flexible piezoelectric film strip is wrapped and covered by the flexible wrapping layer, and the flexible piezoelectric film strip is radially distributed on the plane where the surface of the sensing vibrating diaphragm is located by taking the center of the sensing vibrating diaphragm as the center; the positive and negative electrodes of the flexible piezoelectric film strip are respectively connected to the positive and negative terminals of the electric conductor structure; the piezoelectric fiber is fixed to the sensing diaphragm; the acoustic cavity is a cavity of which the top surface is communicated with the bottom surface, and the ratio of the area of the top surface to the area of the bottom surface is 1-100; the top surface of the acoustic cavity is sealed and covered by the sensing diaphragm, and the bottom surface of the cavity is sealed and covered by the cavity diaphragm. The human physiological signals such as heart sound, breath sound, borborygmus and fetal heart sound can be collected.

The embodiment of the invention discloses a human body state detection method and device, a nose pad module and equipment, the method is applied to the nose pad module, and a physiological sensing module comprises an audio pickup unit, a vibration processing unit and a bioelectric electrode group. The method comprises the following steps: acquiring a multi-modal sensing signal of a nose bridge area of a wearer acquired by the physiological sensing module; wherein the multi-mode sensing signal comprises a breathing audio signal acquired by the audio pickup unit, a bone conduction vibration signal acquired by the vibration processing unit and a bioelectric signal acquired by the bioelectric electrode group; detecting the current human body state of the wearer according to at least two different modal sensing signals in the multi-modal sensing signals; and outputting prompt information associated with the current human body state.

The invention relates to the technical field of medical monitoring, and discloses a chest wall attaching type breath sound collecting system and a control method thereof. The chest wall attaching type breath sound acquisition system comprises a shell, a sound transmission window is arranged on the upper cover, a hydrophobic sound transmission film is covered with the upper cover, a labyrinth type liquid-proof structure and isobaric micropores are arranged in the upper cover, and a coupling cavity is formed below the sound transmission window; the sensing unit is elastically connected with the upper cover through a vibration isolation floating structure; the liquid-proof structure assembly is attached to the bottom film, and a liquid blocking ring is formed in the circumferential direction of the bottom film; the demodulation and acquisition module is used for processing the optical fiber acoustic signal and the MEMS signal to obtain a processed signal; the self-adaptive fusion unit is used for carrying out weight migration and weight fusion on the optical fiber and the MEMS channel to obtain a respiratory phase and event recognition result; and the communication and interface module is used for outputting the identification result to the monitoring / treatment equipment. By adopting the method and the device, the trigger lag and the false trigger rate of the system are improved, and the reliability and the accuracy of the system are improved.

The invention discloses an intelligent auscultation system based on multi-mode biological acoustic analysis and a closed-loop diagnosis method, and relates to the technical field of medical diagnosis equipment. The method comprises the following steps: collecting broadband biological acoustic signals covering heart sound and breath sound; extracting an endogenous physiological clock signal from the broadband biological acoustic signal; separating cardiac cycle acoustic data and respiratory cycle acoustic data based on the endogenous physiological clock signal; generating an initial pathological acoustic probability map; acquiring clinical anchor point data associated with the target patient from an external medical information system; and based on the clinical anchor point data, performing constraint calibration on the initial pathological acoustic probability map to generate a final diagnosis result. Closed-loop calibration is carried out on the pure acoustic analysis model by introducing clinical anchor point data, and the accuracy, reliability and clinical correlation of automatic auscultation diagnosis are improved.

Breathing is fundamental to life. Continuous monitoring of respiratory rate and pattern is crucial for detecting the onset of respiratory failure; however, this vital sign cannot be monitored in most hospital wards. Respiratory distress is caused by dysfunction of oxygenation (insufficient oxygen intake) or dysfunction of ventilation (insufficient carbon dioxide removal). This invention discloses a method and apparatus for monitoring respiratory rate by extracting information from chest acceleration, electrocardiogram, and breath sounds. The invention also discloses a method for quantifying the degree of respiratory deterioration by defining a smart respiratory index, which is defined by combining at least three parameters extracted from physiological measurements such as electrocardiogram, breath sounds, and chest acceleration. These methods are implemented by a small wireless patch attached to the upper part of the patient's chest and communicate with external proprietary software and a monitor via Bluetooth.

The invention discloses a breath sound recognition method and system based on multi-modal data fusion, and relates to the field of breath sound intelligent recognition, firstly, a case text is subjected to structured processing through key entity extraction and natural language recombination, and in combination with audio data enhancement, redundant information is removed, and data quality is improved. Then, a two-channel encoder is used for extracting deep embedded vectors of the text and the audio, and through multi-modal feature splicing and self-encoder joint modeling, deep interaction and feature remodeling between modals are achieved in a hidden layer space; on the basis, a mixed loss function containing reconstruction loss and classification loss is constructed, and model parameters are optimized cooperatively through back propagation. The effectiveness of feature fusion is ensured, the difference between unsupervised feature learning and supervised classification tasks is balanced, and the breath sound recognition precision and robustness of the model in a complex scene are remarkably improved.

The invention discloses a data processing method, a breathing sound production capacity determination system and a medium, and relates to the technical field of sound recognition. And processing the acoustic characteristic data based on the task characteristics of the current breathing sound production function task and the sound production energy threshold to obtain a corresponding effective sound production interval. The sound production energy threshold value is updated according to the real-time environment noise, and the stability and adaptability of effective sound production interval recognition are improved. Considering that fundamental frequency characteristic data of silent friction sound in a normal sound production state does not exist, adaptive processing is realized according to sound production mechanism differences of different function tasks. And determining the sound production capability index and the breathing and sound production coordination result based on the effective sound production interval, determining the corresponding sound production capability index according to different effective sound production intervals, and evaluating the breathing-sound production coordination based on the breathing and sound production coordination result. A unified data processing flow is adopted in the whole process, and compared with conventional manual judgment, subjectivity is avoided, and the accuracy and stability of an evaluation result are improved.

The invention relates to an intelligent breath sound monitoring and recognition system based on multi-point flexible sensing, and the system comprises a plurality of flexible acoustic reception modules, and each flexible acoustic reception module comprises an acoustic sensor used for collecting in-vivo breath sound, original external noise and thoracic motion signals, and a pressure sensor; the signal processing module comprises a signal de-noising module and a wireless communication module, the signal de-noising module takes collected original in-vivo breath sound, external noise and thoracic motion as input, and a de-noised signal is obtained by adopting a three-input collaborative noise elimination algorithm; the intelligent identification module is used for receiving the de-noised signal output by the wireless communication module and carrying out breath sound identification by using an acoustic identification model; the data management module is used for storing output signals of the signal processing module and the intelligent identification module; and the control unit is used for controlling each module to work. Compared with the prior art, the novel wearable breath sound monitoring system has the advantages of comfort, multiple points, noise resistance and the like.