67 results about "Lung sound" patented technology

The invention discloses a lung sound classification method and system based on a lightweight model, and the method comprises the steps: obtaining to-be-processed lung sound audio data, converting the to-be-processed lung sound audio data into a lung sound frequency spectrum image, inputting the lung sound frequency spectrum image into a pre-trained lung sound classification model for classification and recognition, and obtaining a classification result of the to-be-processed lung sound audio data. The lung sound classification model comprises a feature extraction layer, an attention feature enhancement layer and a classification layer; wherein the feature extraction layer is used for performing feature extraction on an input lung audio spectrum image to obtain a lung audio spectrum feature map; the attention feature enhancement layer is used for performing attention feature enhancement processing and time-frequency feature extraction processing on the extracted lung audio spectrum feature map to obtain time-frequency feature data; and the classification layer is used for carrying out classification prediction identification on the time-frequency characteristic data to obtain a classification result of the to-be-processed lung sound audio data. According to the scheme, the accuracy of lung sound classification is improved while computing resource consumption is remarkably reduced.

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 relates to the technical field of respiratory lung sound recognition, in particular to a respiratory lung sound auxiliary recognition method and system for clinical nursing. The invention provides a respiratory lung sound auxiliary identification method and system for clinical nursing, solves the technical problems of data scarcity, complex noise interference and insufficient cross-device generalization ability in lung sound signals through generative data enhancement and cross-modal migration, and combines self-supervised comparative learning and causal feature discovery to identify the respiratory lung sound. Multi-source signal feature fusion and noise robustness improvement are realized, a real-time noise environment is dynamically adapted, the limitation that a traditional method depends on annotation data and feature extraction is easily influenced by hybrid noise is broken through, a full-link closed-loop system from synthetic data generation, cross-modal alignment to causal-driven decision is constructed, and multi-source signal feature fusion and noise robustness improvement are realized. And the accuracy and clinical applicability of clinical lung sound analysis are effectively improved.

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.

1. The name of this design product: digital stethoscope. 2. Purpose of this product design: This product design is used for auscultating human heart and lung sounds. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

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.

The present invention discloses a miniature intelligent lung sound collector and its disease diagnosis auxiliary method and system, which relate to the medical field and solve the problem of poor diagnostic effect of existing lung sound collectors and their disease diagnosis auxiliary methods. The system comprises: step S1: respectively obtaining the lung sound comprehensive frequency deviation and the lung sound period monitoring coefficient to obtain the patient's lung sound monitoring data; step S2: obtaining and analyzing the period characteristic time difference and the symptom monitoring coefficient corresponding to each symptom monitoring sub-period to obtain the patient's symptom analysis coefficient; step S3: obtaining the patient's condition assessment coefficient by analyzing the lung sound comprehensive frequency deviation, the lung sound period monitoring coefficient and the patient's symptom analysis coefficient, obtaining the patient's condition assessment coefficient threshold and performing numerical comparison with the patient's condition assessment coefficient, and performing a diagnosis and assessment of the patient's condition based on the numerical comparison result. The present invention can improve the accuracy and comprehensiveness of disease auxiliary diagnosis.

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 present invention discloses a wristband electronic device, comprising a device body and a wristband, wherein a microphone is provided in the device body, two lugs spaced apart are provided on one side of the device body, a mounting position is formed between the two lugs, and the device body is further provided with a conducting channel, the inner end of the conducting channel is connected to the microphone, and the outer end of the conducting channel is connected to the mounting position; one end of the wristband is inserted into the mounting position, and the wristband is respectively snapped onto the two lugs on both sides in the width direction; a sound collecting structure is provided on the side of the wristband facing away from the wrist, the sound collecting structure is used to be attached to the chest cavity to collect heart sounds and / or lung sounds; the sound collecting structure is connected to the outer end of the conducting channel through its internal channel to transmit the heart sounds and / or lung sounds to the microphone. The technical solution of the present invention can maintain the continuity of the channel, and the wristband can be removed.

The invention provides a wearable wireless stethoscope early warning device for monitoring acute attack of an asthma patient, the wearable wireless stethoscope early warning device comprises a vest, a first binding belt and a second binding belt, the first binding belt and the second binding belt are arranged on two sides of the vest, two adjusting belts are arranged above the vest, and the two adjusting belts are connected with the vest; a miniature sound signal sensor, a miniature lung sound discriminator and a signal microprocessor are arranged in the vest, the miniature sound signal sensor is used for collecting breathing sound signals, and the miniature lung sound discriminator is used for recognizing dry rale, wet rale and normal breathing sound. The breathing sound of an asthma patient, especially a child patient, is monitored in real time, abnormal breathing sound characteristics are accurately recognized, timely early warning of acute attack is achieved, by means of wireless transmission and a remote diagnosis technology, a doctor can remotely obtain illness state information of the patient, timely diagnosis and treatment guidance is provided for the patient, and the patient experience is improved. The risk caused by acute attack of asthma is reduced, and the health management level of patients 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 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 present application relates to a kind of single sensor-based heart-lung multi-band mechanical wave detection device, belong to medical instrument technical field, it is related to heart-lung signal detection technology.By using the first transmission component, piezoelectric film, second transmission component and absolute pressure sensor fixed layer stack composition single mechanical wave sensor, synchronous stable collection is caused by chest wall vibration piezoelectric film and absolute pressure sensor electric signal, by signal amplification circuit processing, transmission to multi-band signal extraction module, digital piezoelectric signal is decomposed to obtain respiratory wave, ultra-low frequency heart wave, heart shock signal and heart-lung sound signal, digital absolute pressure signal is filtered to obtain static pressure value, the degree of chest wall adhesion is embodied, it is realized that single sensor on synchronous extraction heart-lung multi-band mechanical vibration information, to understand heart-lung activity and state provides a kind of efficient, portable mode.

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.

This invention discloses a respiratory monitoring device and its working method, relating to the field of pulmonary function monitoring technology. The respiratory monitoring device includes a flexible chest strap and a flexible shoulder strap. One end of the flexible shoulder strap is sewn to the flexible chest strap. A chest rise and fall acquisition mechanism is installed on one side of the front end of the flexible chest strap, and a lung sound acquisition mechanism is installed on the other side of the front end of the flexible chest strap. A control box is installed on the outer wall of the flexible shoulder strap, and a wire is installed at the upper end of the control box. A respiratory sound acquisition mechanism is installed at one end of the wire. This solution solves the problems of existing pulmonary function monitoring devices being unable to perform real-time monitoring, only being able to monitor pulmonary function through inhaled and exhaled gases, having a single measurement parameter, and low accuracy.

Disclosed is a method for analyzing a heart sound by an electronic device for measuring a preload of a general anesthesia surgery patient including splitting a heart sound and a breathing sound from heart-lung sound data of a patient obtained externally, specifying a breathing section from the split breathing sound, obtaining a heart sound envelope for obtaining a peak signal by converting a heart sound signal, obtaining a heart sound index from the heart sound envelope, and calculating an acoustic variability index (AVI) by analyzing the heart sound index.

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.

Systems and techniques for creating clinically useful emulations of EIT scans by using audio sensors to capture sounds from the thoracic cavity, particularly but not solely lung sounds, where a neural network-based Audio Encoder is, in an embodiment, trained by simultaneously performing both an EIT scan and an audio recording of a cohort of test patients. The EIT scans are passed through an image encoder / decoder pair, each of which can also be neural network-based, and the image encoder creates an embedding representative of the EIT scan. The frequency characteristics of the audio signals are captured as an intermediate representation and supplied to the audio encoder which is trained to map an embedding of the converted audio to the embedding of the EIT scan from the image encoder. At run time audio of a patient is processed through the frequency conversion and supplied to the now-trained Audio Encoder to generate an embedding. The embedding is supplied to the image decoder which produces the EIT emulation for review by a clinician.

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 discloses a patient vital sign intelligent management system based on a wearable device, and relates to the technical field of patient vital sign intelligent management, and the system comprises the following steps: obtaining a lung audio signal and an electrocardiosignal based on the wearable device worn on the chest of a lung cancer patient, and constructing a heart sound trigger point sequence; extracting lung sound signal segments in a preset heartbeat period to obtain a plurality of first signals; performing feature screening on the first signals with the same heartbeat cycle according to a plurality of preset feature dimensions to obtain a plurality of second signals, and extracting a power spectral density curve to obtain a plurality of residual interference energy sequences; and calculating variable coefficients of the residual interference energy sequence in a plurality of adjacent heartbeat cycles, selecting a second signal with the minimum variable coefficient as an optimal lung audio signal, and finally applying the optimal lung audio signal to intelligent vital sign management, thereby solving the problem that in a traditional method, lung sound and heart sound cannot be effectively distinguished. And the problem that lung sound characteristics are weakened by mistake or heart sound is left is solved.

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.

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.