39 results about "Lung sound" patented technology

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 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.

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.

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

The invention discloses a livestock respiratory rate estimation method, and relates to the technical field of animal health monitoring. The method comprises the following steps: collecting a livestock lung sound stream, segmenting into segments according to a fixed window length, and carrying out initial noise reduction; framing endpoints of the segments are detected, starting points and ending points of inspiration segments are marked, and after a sequence is obtained, a small subset and a large subset are divided according to the number of the inspiration segments; screening the effective inspiration section by using the expiration average duration time in the less set, and screening the effective inspiration section by using the difference value between the time interval of two adjacent inspiration sections and the expiration average duration time and the standard difference value in the more set; after the effective inspiration sections are screened out, the breathing frequency is calculated according to the ratio of the time interval between the head and tail inspiration sections of the target section to the number of effective breathing cycles. The accuracy of livestock respiratory rate detection can be effectively improved.

A lung sound analysis system includes an acquisition means for acquiring time-series acoustic signals including lung sounds of a subject who is a heart failure patient, a determination means for determining a pause phase of breathing of the subject, a dividing means for dividing the time-series acoustic signals into those in a period of the pause phase of breathing of the subject and those in a period other than the pause phase, according to a result of the determination, and a detection means for detecting abnormality in the lung sounds from the time-series acoustic signals in the period other than the pause phase after the division. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

The application discloses a lung sound intelligent quality evaluation method and system fusing structural vibration characteristics, three-axis acceleration original data and lung sound audio data are synchronously collected through a bone conduction vibration sensor and a microphone; the mean square deviation and the impact peak value of the combined acceleration are calculated according to the three-axis acceleration original data, and a fuse type signal quality evaluation is carried out; the lung sound audio data is converted into a logarithmic mel spectrum according to the quality evaluation result, and the acoustic characteristics are extracted through a residual network; meanwhile, the three-axis acceleration original data is converted into a vibration spectrum diagram, the mechanical characteristics are extracted through a lightweight convolutional neural network, and the attention mask is generated through Sigmoid activation; the attention mask is multiplied with the acoustic characteristics element by element, and the fusion characteristics are obtained; the fusion characteristics are input into a classifier, and a lung sound pathological classification result is output. The application can eliminate invalid signals in advance, avoid low-quality data from entering the subsequent network, and significantly improve the lung sound classification robustness in a complex environment.

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 pathological characteristic sound detection, and provides a Velcro rale detection method and system based on respiratory auscultation sound, and the method comprises the steps: firstly determining the collection position of lung sound, obtaining the lung sound data of a subject, carrying out the preprocessing, carrying out the initial feature extraction of a preprocessed respiratory sound data set, and obtaining a frequency spectrum representation; then, a pre-trained double-flow multi-view Velcro rale detection model is used for processing: the Velcro rale characteristic acoustic biomarker features represented by the frequency spectrum are enhanced, feature fusion is performed on the enhanced frequency spectrum representation, and multi-view fusion advanced feature representation is obtained; time domain feature representation is extracted from the preprocessed breath sound data set; and finally, integrating the multi-view fusion advanced feature representation and the time domain feature representation to form multi-dimensional time-frequency joint representation, and outputting a detection result. According to the method, the time-frequency distribution mode of the Velcro rale is fully considered, the Velcro rale is accurately and reliably detected, and a non-invasive, economical and efficient means is provided for early screening of IPF.

A lung sound analysis system includes an acquisition means for acquiring time-series acoustic signals including lung sounds of a subject who is a heart failure patient, a determination means for determining a pause phase of breathing of the subject, a dividing means for dividing the time-series acoustic signals into those in a period of the pause phase of breathing of the subject and those in a period other than the pause phase, according to a result of the determination, and a detection means for detecting abnormality in the lung sounds from the time-series acoustic signals in the period other than the pause phase after the division. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

The purpose of this invention is to analyze the vibrations, including sound emitted by a subject, to detect respiratory diseases in the subject at an early stage and to recommend the subject to a medical institution. The information processing system (100) includes: a signal acquisition unit (201) that acquires a feature signal, including at least one of a heartbeat signal, a respiratory vibration signal, and a body movement signal, and a lung sound signal, extracted from a detection signal output by a sensor (11) that detects vibrations emitted by a subject; a first prediction unit (202) that outputs first prediction information based on the feature signal and the lung sound signal, the first prediction information including at least one of first information related to the possibility that the subject has a respiratory disease and second information related to the respiratory disease suffered by the subject; and an output unit (206) that outputs at least one of the first prediction information and a medical institution recommendation information generated based on the first prediction information.

1. Name of the product in this design: Electronic stethoscope. 2. Purpose of this design: This design is used to collect and receive heart and lung sounds. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

An analysis device includes a detection unit that detects abnormality in lung sounds for each of auscultation positions on the basis of time-series acoustic signals including lung sounds of each of the auscultation positions; a determination unit that determines severity of heart failure of a patient on the basis of a detection result of abnormality in the lung sounds of each of the auscultation positions detected by the detection unit and condition information representing the condition of the patient; and a decision unit that decides an instruction to be output to the patient on the basis of a result of the determination by the determination unit.

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.

An electric sound stethoscope with double-channel compatible and collection switch relates to the field of medical auscultation, which is configured with double-channel compatible and convenient collection switch, analyzes and compares sound and sound wave pattern functions, and outputs two high-definition loudspeaker work of audio amplifier modules in two ways to connect two audio boxes, which is a new innovation for diagnosing heart and lung diseases. The excellent electric sound quality presents the extremely distinctive creative color of the system. The system includes electronic or traditional auscultation head to collect sound signals, and the sound signals are converted into electric sound signals by a microphone. The auscultation includes collecting heart and lung sound, and independently setting a microphone channel or a microphone wired socket interface with a convenient switch, forming the electric sound signal of the heart and lung auscultation collected by the convenient switch and the auscultation method to suppress howling. The electric sound signal enters the functional module circuit for processing through the appropriate transmission mode, amplifies the electric sound signal to drive the loudspeaker to broadcast and listen to the auscultation.

Disclosed are an interaction apparatus of wrist-worn device and a wrist-worn device. The interaction apparatus of wrist-worn device includes a main structure including a housing and a mainboard disposed on the housing, and a signal acquisition portion disposed protruding from a side peripheral surface of the housing, the signal acquisition portion includes a physiological sound sensor used for detecting heart sound signals or lung sound signals. The physiological sound sensor collects a vibration signal of the heart or lung and transmits the signal to the mainboard, the mainboard processes the signal to monitor the heart or lung. According to the present disclosure, the cost of replacing the wristband is reduced; the original inner structure of the housing is prevented from being modified; and when the hand is lifted, the signal acquisition portion can be directly attached to the heart position or the lung position for signal detection.

A lung sound analysis system includes an acquisition means for acquiring time-series acoustic signals including lung sounds of a subject who is a heart failure patient, a determination means for determining a pause phase of breathing of the subject, a dividing means for dividing the time-series acoustic signals into those in a period of the pause phase of breathing of the subject and those in a period other than the pause phase, according to a result of the determination, and a detection means for detecting abnormality in the lung sounds from the time-series acoustic signals in the period other than the pause phase after the division. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

The invention discloses a cardiopulmonary sound detection device control method and device, a cardiopulmonary sound detection ring and a storage medium, relates to the technical field of wearable equipment, and discloses a cardiopulmonary sound detection device control method, the cardiopulmonary sound detection device comprises an outer shell and an inner shell, the outer shell and the inner shell are connected and enclose to form an annular accommodating cavity; the heart and lung sound detection device comprises an annular containing cavity, and a PCB mainboard, a pressure sensing chip and an inertial measurement unit which are arranged in the annular containing cavity, and the control method of the heart and lung sound detection device comprises the steps that when a pressure detection process is triggered, the deformation quantity, detected by the pressure sensing chip, of the PCB mainboard and inertial parameters detected by the inertial measurement unit are obtained; updating a preset force conduction coefficient based on the inertial parameter to obtain a target force conduction coefficient; and calculating a pressure detection value of the heart and lung sound detection device according to the target force conduction coefficient and the deformation quantity. The calculation result of the pressure detection value corresponds to the real deformation data, the pressure value is accurately judged, and the accuracy of heart and lung sound detection is improved.

A lung sound analysis system includes: an acquisition unit that obtains time-series acoustic signals including lung sounds from a heart failure patient; a determination unit that identifies the pause phase of the patient's breathing; a dividing unit that separates the time-series signals into those during the pause phase and those during other phases, based on the determination; a calculation unit that computes an index value representing the quality of the signals outside the pause phase, using the intensity of signals from both phases after separation; and a warning unit that issues alerts based on the calculated index value. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

The invention relates to the technical field of chronic respiratory disease diagnosis and treatment, and discloses a chronic respiratory disease diagnosis and treatment management system based on artificial intelligence. The system comprises a breath data dynamic acquisition module, a pathological feature extraction module, a multi-dimensional illness state evaluation module, an abnormal focus positioning module and an individualized diagnosis and treatment strategy generation module. According to the system, breathing waveform, oxyhemoglobin saturation time sequence and lung sound feature data are collected in real time through a multi-mode sensor, key pathological features are extracted through time-frequency conjoint analysis, trend decomposition and spectrum energy distribution calculation, and then a comprehensive disease severity score is generated through a deep feature fusion network. And matching and marking potential focus positions by combining a pathological feature library, and finally dynamically adjusting the drug dosage and a rehabilitation training scheme. According to the system, intelligent management of the whole process of chronic respiratory disease diagnosis and treatment is achieved, comprehensive support is provided for diagnosis and treatment decisions by means of multi-dimensional data integration and accurate analysis, and the clinical requirement for individual management of chronic respiratory diseases is met.

A lung sound analysis system includes: an acquisition unit that obtains time-series acoustic signals including lung sounds from a heart failure patient; a determination unit that identifies the pause phase of the patient's breathing; a dividing unit that separates the time-series signals into those during the pause phase and those during other phases, based on the determination; a calculation unit that computes an index value representing the quality of the signals outside the pause phase, using the intensity of signals from both phases after separation; and a warning unit that issues alerts based on the calculated index value. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

The application discloses a lung sound analysis method and device, an electronic device and a storage medium, which can be widely applied to the technical field of lung sound data processing. After obtaining original breathing data, the application processes the original breathing data to obtain a breathing time domain graph, then inputs the breathing time domain data into a preset AR model to output a multi-order time sequence curve graph, and processes the multi-order time sequence curve graph through a window function to obtain a breathing frequency spectrum graph, so that the efficiency and accuracy of frequency calculation in a short period can be accelerated, and then lung sound feature analysis can be performed according to the breathing frequency spectrum graph, and the accuracy of the lung sound analysis result can be effectively improved.

1. Name of the product in this design: Human Disease Stethoscope (WDS-300). 2. Purpose of this design: This design is used for auscultating human heart and lung sounds. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

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.

<pre>The Intelligent Monitoring System is an integrated solution for managing and monitoring chronic respiratory diseases. It combines a patient-facing mobile application with a platform dedicated to healthcare professionals, enabling continuous monitoring and collaborative management. The system collects physiological parameters—such as heart rate, oxygen saturation (SpO₂), and electrocardiogram (ECG) and photoplethysmogram (PPG) signals—using smartwatch sensors. It also records lung sounds via an electronic stethoscope and measures environmental data (air quality, temperature, and humidity) using specialized sensors and a public API. A smart inhaler is integrated to guide patients in its use, thereby boosting treatment adherence and ensuring greater therapeutic efficacy. By leveraging Edge AI, the system predicts key parameters like respiratory rate and analyzes lung sounds directly within the mobile app to detect respiratory abnormalities. Relying on edge computing, it offers real-time processing, reducing cloud dependency while maintaining high accuracy. The system helps patients quickly recognize critical symptoms, monitor vital signs and environmental triggers, and track treatment adherence. Data is shared instantly and securely with healthcare professionals via the platform, facilitating rapid interventions and reducing the need for emergency care. By integrating these features, the system promotes proactive respiratory disease management, prevents exacerbations, and improves patients' quality of life.< / pre>

The present application relates to the technical field of lung sound data processing, in particular to a lung sound data similarity matching method and system, the method comprising: collecting and preprocessing lung sound data to be matched; decomposing the preprocessed lung sound data to be matched based on wavelet transform to form a feature matrix of the preprocessed lung sound data to be matched; constructing a lung sound data similarity matching model based on deep learning, training the lung sound data similarity matching model based on the feature matrix; performing similarity matching on the lung sound data to be matched based on the trained lung sound data similarity matching model; and performing accuracy analysis on the similarity matching result. The present application decomposes the lung sound data to be matched by wavelet transform, extracts a feature matrix with strong feature information, constructs a lung sound data similarity matching model for similarity matching, improves the processing efficiency of lung sound data, and further improves the diagnostic efficiency of lung auscultation.

The application relates to the technical field of medical treatment, in particular to a heart-lung function failure intensive care device, which comprises a monitoring bin and an inlet and outlet arranged at one end of the monitoring bin, a supporting plate is fixedly installed at the bottom of the inner wall of the monitoring bin, a moving table penetrating through the inlet and outlet is movably connected to the supporting plate, a displacement assembly is arranged at the bottom of the moving table, a hinge seat is fixed at one end of the middle position of the top of the moving table, first and second closing plates are rotationally connected to the two ends of the hinge seat, a butt joint groove is arranged at the top of each of the first and second closing plates, and a heart-lung sound monitoring assembly is arranged in the butt joint groove. The heart-lung function data of a critical patient can be collected, the technical problem that accurate heart-lung function data cannot be collected in real time in the prior art is solved, the patient can be monitored conveniently, the multiple monitoring mode is realized, and the monitoring process is not affected by the damage of the monitoring part in the long-time monitoring process.