177 results about "Lung sound" patented technology

A lung sound diagnostic system for use in collecting, organizing and analyzing lung sounds associated with the inspiration(s) and expiration(s) of a patient. The system includes a plurality of transducers that may be placed at various sites around the patient's chest. The microphones are coupled to signal processing circuitry and A / D converters which digitize the data and preferably provides the digital data to a computer station. A data collection and organization program, executing on the computer station, organizes and formats the data into a combination display for display or printing. The combinational display includes at least two display elements. In a first display element, the data is shown for both inspiration and expiration combined in a first time scale. In a second display element, the data for inspiration and expiration are shown individually in a second time scale that is time-expanded relative to the first time scale. The system may also include application programs for detecting and classifying abnormal sounds. The resulting information may be displayed in a variety of formats to facilitate diagnosis. Additionally, the system may include an analysis program for comparing selected criteria corresponding to the detected abnormal sounds with predefined thresholds in order to provide a likely diagnosis.

Adventitious lung sounds indicative of lung congestion are detected using an implantable sensor. The sensor is adapted to be positioned adjacent to a pulmonary system and to output signals indicative of lung sounds in response to pulmonary system activity. A controller receives the signals and processes the signals to detect the presence of adventitious lung sounds. A respiratory cycle sensor operating in conjunction with the lung-sound sensor enables classification of an adventitious lung sound according to its time occurrence within the respiratory cycle. Posture sensing in conjunction with lung-sound sensing provides valuable additional information as to the severity of the lung congestion.

The present invention discloses an electronic stethoscope with a Piezo-Electrical Film contact microphone comprising a stethoscope head with a Piezo-Electrical Film contact microphone inside, and the stethoscope head is electrically connected to a circuit and a microcontroller unit (MCU). The microcontroller unit is connected to a front-end operational amplifier (OP-amp) circuit, a wave filter circuit, and a transmit circuit, such that when the stethoscope is used, the weak sound signal received by contacting stethoscope head with a human body is sent to the OP amplifier. The amplified sound signal (such as heart sound and lung sound) selectively measured by the switch module is processed by the microcontroller unit and the wave filter. The filtered sound signal is sent to a transmit / receive circuit, so that the wave filter circuit can filter the noise of the sound signal produced by human bodies under the control of the microcontroller unit, and medical people can make correct diagnostics based on the correct sound received through the transmit / receive circuits.

The invention provides an electronic stethoscope. The electronic stethoscope comprises a stethoscope head for being contacted with the body surface during auscultation so that acoustic signals sent by organs and radiated by the body surface generate resonance and the resonance acoustic signals are converted into electric signals, a host machine for amplifying and filtering the electric signals converted by the stethoscope head and converting the electric signals processed into acoustic signals, and a loudspeaker for playing the acoustic signals converted by the host machine. The electronic stethoscope provided by the invention can monitor and record sounds at various frequencies sent by various organs such as heart sounds, bowelsounds, lung sounds, and fetal heart sounds, is suitable for diagnosing various diseases, and is simple in structure and using method, and can effectively isolate external noises so as to improve the diagnosing accuracy.

The present invention relates to device, system and method for detecting a cardiac and / or respiratory disease of a subject. The proposed device comprises a sound input (20) for obtaining a sound signal representing sounds generated by the subject's body; a motion input (21) for obtaining a motion signal representing motions generated by the subject's body; and a processor (22) for processing the obtained sound signal and motion signal. This processing includes identifying inhalation and / or exhalation periods of the subject based on the motion signal, detecting abnormal lung sounds during inhalation and / or exhalation periods based on the sound signal, determining abnormal lung sound characteristics of the detected abnormal lung sounds, determining breathing characteristics of the subject's breathing based on the sound signal, determining the phase of the abnormal lung sounds in the inhalation-exhalation cycle, and detecting a cardiac and / or respiratory disease of the subject based on the determined abnormal lung sound characteristics, the determined breathing characteristics and the determined phase of the abnormal lung sounds in the inhalation-exhalation cycle.

A lung sound diagnostic system for use in collecting, organizing and analyzing lung sounds associated with the inspiration(s) and expiration(s) of a patient. The system includes a plurality of transducers that may be placed at various sites around the patient's chest. The microphones are coupled to signal processing circuitry and A / D converters which digitize the data and preferably provides the digital data to a computer station. A data collection and organization program, executing on the computer station, organizes and formats the data into a combination display for display or printing. The combinational display includes at least two display elements. In a first display element, the data is shown for both inspiration and expiration combined in a first time scale. In a second display element, the data for inspiration and expiration are shown individually in a second time scale that is time-expanded relative to the first time scale. The system may also include application programs for detecting and classifying abnormal sounds. The resulting information may be displayed in a variety of formats to facilitate diagnosis. Additionally, the system may include an analysis program for comparing selected criteria corresponding to the detected abnormal sounds with predefined thresholds in order to provide a likely diagnosis.

A system and method are provided for diagnosis of animal respiratory diseases using auscultation techniques. Animal lung sounds are recorded and digitized. Lung sounds are obtained by an electronic digital stethoscope or a wireless audio digital recording unit. The sounds are stored as digital data, and one or more algorithms are applied to the data for producing an output to the user indicative of the health of the animal. The acoustic characteristics of the sound are compared with baseline data in the algorithms. One embodiment includes a digital stethoscope with an integral display. Another embodiment provides a system for gathering information about an animal to include not only auscultation data, but also information from other field devices such as temperature probes or weigh scales. The combined information can be analyzed by system software to generate detailed information to a user to include a diagnosis and recommended treatment options.

The invention relates to a method and a system for identifying heart-lung sound signals. The method includes the steps of acquiring heart / lung sounds initial detection signals, acquiring correspondingenvironment sound signals, and subjecting the heart / lung sounds initial detection signals to differential noise reduction processing, so as to prevent the influence of the peripheral environment on the cardio-pulmonary sound signals, prevent influence of signals irrelevant to the cardio-pulmonary sound signals on the identification precision, and effectively improve the collection and identification precision of the cardio-pulmonary sound signals. The heart sound simulation signals is processed through combination of EMD (Empirial Mode Decomposition) and a high-order shannon entropy algorithm, and a wavelet decomposition is used for processing the lung sound digital signals. Accurate recognition of the characteristics of the heart sound signals and the lung sound signals is achieved, andthe identification steps of the cardio-pulmonary sound signals are effectively simplified. The real-time performance is higher, and the cost is low.

The invention relates to a heart-lung sound signal classification method, a detection method, a medium and a computer device. The heart-lung sound signal classification method includes the steps of acquiring a heart-lung sound signal to be classified, and converting the heart-lung sound signal by using a time-frequency analysis method to obtain a time-frequency signal; and performing dimensionality reduction on the time-frequency signal by using a matrix decomposition method to obtain an independent component signal; processing the independent component signal according to time-frequency masktechnology to obtain a heart sound spectrum signal and a lung sound spectrum signal; and performing inverse transform corresponding to the time-frequency analysis method on the heart sound spectrum signal and the lung sound spectrum signal to obtain a heart sound signal and a lung sound signal. In this way, the classification of the heart sound signal and the lung sound signal in the heart-lung sound signal can be realized by means of signal processing, the refined independent examination and analysis of the heart sound signal and the lung sound signal are facilitated, manual auscultation is unnecessary, examination items for heart and lung diseases can be simplified, and the efficiency of heart and lung disease examination can be therefore increased.

A system and method are provided for diagnosis of bovine respiratory diseases using auscultation techniques wherein the acoustic characteristics of a recorded spectrogram is compared with existing data enabling a caregiver to make a diagnosis for a diseased animal. Lung sounds are obtained by use of an electronic stethoscope, and the sounds are stored as digital data. Signal conditioning is used to place the data in a desired format and to remove undesirable noise associated with the recorded sounds. An algorithm is applied to data, and lung scores are calculated. The lung scores are then categorized into various levels of perceived pathology / disease within the animal based upon baseline data that categorizes the lung scores. From the lung scores, a caregiver may then associate a diagnosis, prognosis, and a recommended treatment. Analysis software in the present invention generates the lung scores from the recorded sounds, and may also provide the user a visual display of presumptive diagnoses as well as recommended treatments.

A system and method are provided for diagnosis of animal respiratory diseases using auscultation techniques. Animal lung sounds are recorded and stored as digitized data. Algorithms are applied to the data producing an output indicative of the health of the animal. Another method determines effectiveness of antibiotics administered to animals based upon observed relationships between lung score categories obtained from auscultation. A comparison is made between sample populations of animals that receive different classifications of antibiotics, and this data is compared to lung score categories observed for each of the animals that received the antibiotics. A statistical analysis is conducted to confirm statistical differences in case fatality rates between lung score categories. Insignificant differences in mortality rates across a range of lung score categories indicates the particular antibiotic administered is not effective, whereas a reduced fatality rate associated with lower lung scores indicates a level of antibiotic effectiveness.

A portable monitoring system for recognizing wheeze in lung sounds can detect the occurrence of wheeze from the neck of an asthmatic. The portable monitoring system comprises an acoustic sensor, a signal processor and a wireless signal transmission module, a remote analyzer and an alarm generator. The acoustic sensor is placed next to the windpipe of the asthmatic to collect the acoustic signals when he breathes. The signal processor and remote analyzer analyze the acoustic signals to recognize whether the wheeze, in the form of specified signals, exists. If the wheeze is found, the wireless signal transmission module or the remote analyzer directly instructs the alarm generator to generate an alarm so as to notify someone to give medical treatment to the asthmatic.

The invention relates to a coronary heart disease surveillance and diagnosis device. The coronary heart disease surveillance and diagnosis device comprises a heart-lung signal collection device and aheart-lung signal analyzing device, wherein the heart-lung signal collection device collects heart-lung sound overlapped signals and sends the heart-lung sound overlapped signals to the heart-lung signal analyzing device, and the heart-lung signal analyzing device separates the heart-lung sound overlapped signals according to the short-time Fourier transform method so as to obtain an original heart sound signal; the original heart sound signal is reduced according to the double-threshold method, the peak picking method and the endpoint algorithm so as to obtain a phonocardiogram; the phonocardiogram is subjected to complex wavelet transformation so as to extract a signal envelop of the complex wavelet transformation; the signal envelop is trained according to a trained BP neural network soas to obtain an analysis result, and the analysis result is output. Based on the characteristic that in the early period of the coronary heart disease, high-frequency pathological murmur can occur inthe heart sound signal, the heart-lung sound overlapped signals are analyzed by using the short-time Fourier transform method, it is noninvasively and early diagnosed whether an examinee suffers fromthe coronary heart disease or not, and the diagnosis efficiency is high.

A device for sensing lung sounds, comprising: a piezoelectric sensor comprising an electrical conductive plate attached to a piezoelectric material, said sensor encased in a body structure; a first electric wire connected to the piezoelectric material on the opposite side of said plate; a second electric wire connected to said plate; a connector connected to the other ends of said first and second electric wires; and an adhesive layer connected to the surface of said plate on the side opposite to the piezoelectric material, said adhesive layer facing away from said plate; said device adapted to provide electrical signals representing vibrations present on the surface of a object when it is attached to said object surface with said adhesive layer; said electrical signals resulting from vibrations on the object surface, wherein stress applied on the piezoelectric material generates electrical voltage-difference on both sides of the piezoelectric material, creating voltage build-up on said first and second electric wires.

The invention relates to a portable and visual Bluetooth electronic stethoscope capable of separating heart and lung sounds. The traditional stethoscope is difficult to capture some weak and very important biological sounds made by the internal organs of a human body. A heart and lung sound sensor and the input end of an active filter circuit in the portable and visual Bluetooth electronic stethoscope are connected through signals, the output end of the active filter circuit is connected with the input end of a filter interface circuit through signals, the output end of the filter interface circuit is respectively connected with an audio power amplifying circuit and a level switching circuit through signals, the audio power amplifying circuit is connected with earphones through signals, the level switching circuit is connected with a master control unit circuit through signals, and a power circuit provides power for the active filter circuit, the filter interface circuit, the audio power amplifying circuit, the level switching circuit and the master control unit circuit. When selectively and individually hearing the heart or lung sounds, the electronic stethoscope analyzes the heart or lung sounds in real time by means of the Bluetooth wireless connection with a computer.

The invention discloses a remote intelligent auscultation service system for self monitoring of heart and lung diseases. The system comprises an electronic auscultation device, a server side, an auscultation terminal and a user client; the electronic auscultation device is used for listening heart and lung sound of a human body, and sending information to the server side; the server side is used for comparing the received heart and lung sound of the human body with standard voice in a database, sending the result to the auscultation terminal, receiving information transmitted by the auscultation terminal, and sending the information to the user client; the auscultation terminal is used for judging received heart and lung data, and sending the result to the server side; the user client is used for inputting user basic information and physical condition, and receiving the auscultation result sent by the server side. The system provided by the invention can remotely auscultate the heart and lung sound of a patient, and send the auscultation result to the user, so that the user can know own physical condition in real time, and the system is simple in structure and is convenient for remote communication between the user and a doctor.

The present invention relates to a lung sound classification method, system and application based on a convolutional neural network. An image identification convolutional neural network is applied tolung sound classification, a general model is used to solve huge defects that a traditional algorithm has manual extraction of features and join experience limitation conditions and cannot be generally suitable for all the samples, and solve problems that a traditional algorithm cannot be enhanced with a data set and cannot be subjected to autonomic learning enhancement.

The invention discloses a soft close-fitting intelligent health monitoring vest, and belongs to the field of health monitoring equipment. The scheme includes that a flexible printed circuit board is arranged at a front chest of a vest body, a sensor module, a control core device and a key module are arranged on the flexible printed circuit board, the sensor module which is in contact with the skins of a wearer is provided with non-sticky conductive rubber jackets, and portions, except for the sensor module, of the flexible printed circuit board are in contact with the skins of the wearer via lint after being insulated and isolated; the sensor module is used for monitoring electrocardiogram signals, heart and lung sound signals and body temperature signals and transmitting the electrocardiogram signals, the heart and lung sound signals and the body temperature signals to the control core device; external instruments can be transmitted to the control core device by the key module; data can be exchanged between the control core device and a mobile phone in a wireless mode, can be uploaded to a cloud data management system by the mobile phone and can be called by doctors or users or family members. The soft close-fitting intelligent health monitoring vest has the advantages that the soft close-fitting intelligent health monitoring vest which is an intelligent health monitoring garment can be used by old people, old people can wear the intelligent health monitoring garment comfortably, influence on normal life can be prevented, and the intelligent health monitoring garment is free of maintenance or complicated operation; the vest is used for monitoring the health of old and weak people.

A system and method for providing a medical simulation system that adds high-fidelity features to manikin simulators and standardized patients. The medical simulation system includes wearable components that contain modules for simulating pulses, heart and lung sounds, and breathing motion. The wearable components may be coupled to a vital signs display and may be incorporated into a manikin simulator or worn by a standardized patient. The medical simulation system includes an isolation component that isolates the wearable components from the manikin or standardized patient, and isolates the manikin or standardized patient from the wearable components.

The invention provides a portable wireless electronic stethoscope which comprises a stethoscope head and a processing device, wherein the stethoscope head can be adhered to the surface of a human body, and the processing device can be in wireless communication with the stethoscope head. When in use, an acoustic sensor of the stethoscope head senses a lung sound signal, and the lung sound signal is transmitted to the processing device after being subjected to amplification, filtration and analog-to-digital conversion; and after the processing device receives the lung sound signal, the lung sound signal and a cardiac sound signal accessed through a cardiac sound signal port are subjected to adaptation operation, the cardiac sound signal in the lung sound signal is filtered out, and then the lung sound signal the cardiac sound signal of which is filtered out is displayed, thus realizing the purpose of monitoring the lung sound signal. Since the stethoscope head is in wireless communication with the processing device, cross infection between a doctor and a patient when the existing stethoscope is utilized can be effectively avoided, and the use is safer.

The invention discloses a heart sound, lung sound and fetal heart sound acquisition and health management system, which comprises an acquisition terminal, a transmission system, a platform processing system and a storage and display system, wherein the acquisition terminal is used for acquiring the heart sound or lung sound or fetal heart sound condition of a human body, and converting the heart sound or lung sound or fetal heart sound condition into an electric signal; the acquisition terminal is in communication connection with the transmission system through a wired network or a wireless network, and the transmission system is a mobile phone; the transmission system is in communication connection with the platform processing system through the wireless network, and the platform processing system is a health management service cloud platform; the storage and display system is a mobile phone, and is in communication connection with the platform processing system through the wireless network. According to the system, the heart sound, the lung sound and the fetal heart sound of the human body are acquired by the acquisition terminal or a microphone, are transmitted to the mobile phone after being subjected to processing such as data conversion and amplification filtration, and are transmitted to the health management service cloud platform through the mobile phone, and a health management report of the human body is obtained for the timely query of the human body through the mobile phone.

A network-type instrument for detecting blood pressure, cardiac rhythm, body temp and cardiopulmonary murmur is composed of power supply, CPU, information acquisition unit, information processing unit, operating unit, display unit, loudspeaker and communication unit. Said information can be displayed on display device or transmitted via network to remote monitor center.

The invention discloses a non-negative matrix factorization-based heart and lung sound separation method. The method comprises the steps of firstly filtering a mixed signal of heart and lung sounds in a time domain to obtain a priori heart sound signal and a priori lung sound signal; secondly performing short-time Fourier transform and sparse non-negative matrix factorization on the priori heart sound signal and the priori lung sound signal; and finally obtaining a basis matrix of the mixed signal. The sparse non-negative matrix factorization process is equivalent to a learning process of the basis matrix, and has a clustering effect; the basis matrix can be mined effectively from the priori heart sound signal and the priori lung sound signal; and a heart sound signal and a lung sound signal are accurately separated from the mixed signal of the heart and lung sounds in the time domain according to the basis matrix in the subsequent steps. The invention furthermore discloses a non-negative matrix factorization-based heart and lung sound separation apparatus, which has the abovementioned beneficial effects.

The invention discloses a digital lung sound characteristic dimension reducing method based on a relevance vector machine, wherein the method belongs to the field of digital medical technology. According to the method of the invention, a characteristic space is mapped to a sample space; a mutual information characteristic kernel function is utilized for representing association strength between lung sound sample characteristic attributes; and finally dimension reduction of the lung sound characteristic is finally realized. The method comprises the steps of selecting lung sound data from a lung sound database as a lung sound sample, and establishing the relevance vector machine; establishing a lung sound characteristic vector sample set; and reducing the dimension based on an RVM lung sound characteristic vector. The digital lung sound characteristic dimension reducing method has advantages of high robustness, sparsity, low sensitivity to data noise, effective reduction of uncertainty caused by acquired noise, and high suitability for processing high-dimension lung sound characteristics. According to the digital lung sound characteristic dimension reducing method, mutual information between the lung sound characteristics is utilized as a kernel function; association strength between the characteristics is sufficiently considered in the model; not only is conciseness of a dimension reduction result ensured, but also completeness of reserved characteristics is ensured.

The invention discloses a dual-microphone noise reduction method and device applied to medical acoustic signals such as heart and lung sounds, fetal heart sounds and the like. The dual-microphone noise reduction method for the medical acoustic signals comprises steps as follows: acquiring medical acoustic signal data with noises and ambient noise data, and processing the medical acoustic signal data and the ambient noise data respectively to obtain a time-frequency unit; calculating a characteristic value for the time-frequency unit; generating a masking value according to the relation between the characteristic value and a masking threshold, and marking a part, which mainly comprises the medical acoustic signals, of the time-frequency unit; masking the time-frequency unit of the medical acoustic signal data with the noises according to the generated masking value, reserving the part which mainly comprises the medical acoustic signals, and reconstructing the masked data to obtain a noise reduction result. According to the noise reduction method, the medical acoustic signals with the noises and ambient noises are acquired respectively by the aid of dual microphones, and the medical acoustic signals and the ambient noises are effectively separated.

The invention discloses an auscultation system for heart and lung sound separation based on deep learning; bone conduction is used for auscultation. The auscultation device includes an auscultation head, a signal acquisition module, a heart and lung sound signal separation module, a memory, a user interface, an output driving unit and a bone conduction unit. The signal acquisition module acquires heart and lung sound signals and digitizes the heart and lung sound signals, and the heart and lung sound signal separation module adopts a time-frequency masking deep circulatory neural network for heart and lung sound signal separation; the user interface can select heart-lung sound modes and store expert tags. The output driving unit receives heart or lung sound time-domain signals separated from previous stage for power amplification, and drives the bone conduction unit to generate vibration. The auscultator can automatically separate clean heart and lung sounds from the overlapped heart and lung sounds, facilitate data acquisition and post-analysis, and enhance the auscultation experience.

The invention discloses a single-channel heart and lung sound separation method and system. The method comprises the following steps of step 1, processing the collected heart and lung sound mixed signals x(t) to obtain the time-frequency spectrum X of the heart and lung sound mixed signals x(t); step 2, taking the column average of 20 Hz to 100 Hz portions of the time-frequency spectrum X and obtaining an amplitude model time series template H0 of heart sounds; step 3, constructing heart sound time series structure regularization terms according to the amplitude model time series template H0 of the heart sounds, performing non-negative matrix factorization on the time-frequency spectrum X, extracting the estimation Xc of the time-frequency spectrum of the heart sound signals and the estimation Xr of the time-frequency spectrum of lung sound signals; and step 4, reconstructing time domain signals of the heart sounds and the lung sounds according to the estimation Xc of the time-frequency spectrum of the heart sound signals and the estimation Xr of the time-frequency spectrum of the lung sound signals. The single-channel heart and lung sound separation method and system can effectively separate overlapping components of heart sounds and lung sounds in a frequency domain, require only single-channel heart and lung sound mixed signals as input to achieve effective separation of heart and lung sounds, have strong clinical applicability, do not need clustering, and have stable effect of separating the heart and lung sounds.

The invention relates to a rale detection method and device. In one embodiment, the method includes the steps that a to-be-detected lung sound signal is received and subjected to filtering processing so that a filtered signal can be output; time frequency conversion is performed on the filtered signal, a time frequency signal is obtained, the signal is subjected to integral along the frequency axis so that a time edge signal can be obtained, and the signal is subjected to integral along the time axis so that a frequency edge signal can be obtained; a first characteristic value of the to-be-detected lung sound signal is obtained according to the frequency edge signal, and a second characteristic value and / or a third characteristic value of the to-be-detected lung sound signal is obtained according to the time edge signal; two or more of the first characteristic value, the second characteristic value and the third characteristic value are utilized for constructing a characteristic vector of the to-be-detected lung sound signal, and the characteristic vector is sent into a trained classifier to be recognized, and a recognition result is output. According to the rale detection method and device, the lung sound signal is used for training the classifier, the classifier is used for recognizing the to-be-detected lung sound, the real-time detection efficiency is improved, meanwhile, the calculation amount is reduced, and good application prospects are achieved.

The invention discloses a lung sound feature classification system and method based on deep learning and a cloud platform. The system comprises a lung sound data acquisition module, a data preprocessing module, a spectrogram image data acquisition module, a feature training module, a feature classification module and a result feedback module. The lung sound can be obtained through the electronic stethoscope, the problems that lung sound feature obtaining is inaccurate due to manual auscultation, and an X-ray chest X-ray diagnostic method and a CT diagnostic method injure the body are solved, the features of the lung sound can be obtained in an unsubjective mode, lung sound feature data are provided for doctors, and the doctors can perform subsequent diagnosis conveniently.