12 results about "Respiratory sounds" patented technology

The present invention relates to an operating method of a lung monitoring system operated by at least one processor, the operating method comprising the steps of: acquiring a plurality of electrical impedance tomography (EIT) images of the chest of a patient and a plurality of respiratory sound intensity images generated on the basis of respiratory sound signals acquired from the chest of the patient; mapping, on the basis of a plurality of pieces of respiratory interval information acquired by binning a respiratory signal of the patient, an EIT image and a respiratory sound intensity image corresponding to each respiratory interval to each other; and inputting, into a respiratory anomaly prediction model, at least one pair of the EIT image and the respiratory sound intensity image included in each respiratory interval, to acquire a prediction result for respiratory anomalies of the patient.

An artificial intelligence-based system and method for scalable screening of individuals for respiratory infection, such as COVID-19. The system is trained to distinguish distinct latent features of cough sounds produced by a COVID-19 infected person from cough sounds produced by patients suffering from any other respiratory infection or involuntary cough sounds produced by a healthy person. Cough sound samples from individuals can be remotely collected and evaluated by the system for likelihood of the COVID-19 infection. Additionally, images of affected body parts, biomarkers, metadata, and other respiratory sound samples can also be used for screening.

The application relates to the technical field of atomizer control, and discloses a child intelligent atomizer capable of quantitatively guiding a breathing mode and a control system, which comprises a user analysis unit used for receiving real-time breathing frequency and body temperature data of a user through a mobile terminal, comparing and analyzing the collected real-time breathing frequency and body temperature data of the user with a standard database, identifying the use condition of the user, and generating a control instruction; the application increases the interactive feeling through voice guidance, pacifies the emotion, guides the breathing, and makes the small children naturally calm down through music; the light prompts abnormal conditions, sets up the atmosphere, effectively reduces the fear of the children for treatment, compared with common atomizers, the application can prompt correct breathing modes, the medicine deposition effect is better, in addition, the music is self-defined, the childlike and lovely appearance is matched, the atomizer is more like a music toy, the attention of the children can be transferred from the discomfort of treatment, and the cooperation degree is improved.

The embodiment of the application discloses a kind of master identification method and terminal equipment, it is applicable to computer technology field, the method comprises: terminal equipment responds to the unlocking operation of target content to the user data of the user to be measured of the user to be measured, wherein, target content is in the state of being locked, user data includes at least one of the following: the face image of the user to be measured, the respiratory audio data of the user to be measured, the IMU data of the user to be measured and the touch screen data of the user to be measured;According to user data and pre-recorded master template, master identification is carried out, master template includes at least one of the following: face template, respiratory sound feature template, cross-modal feature template, cross-modal feature template is used to describe the comprehensive characteristics of master in multiple modes;In the case where master is identified, target content is unlocked.The embodiment of the application can be used in the process of terminal equipment, and master identification is carried out based on respiratory sound feature, to protect the safety of master private information.

This invention provides a controllable generative enhancement method for respiratory sound categories based on discrete tokens and Transformer, belonging to the interdisciplinary field of medical signal processing and artificial intelligence. The method employs a two-stage framework: In the first stage, a conditional vector quantization autoencoder is trained, injecting pathological category labels during encoding and introducing multi-scale category prototypes based on statistical analysis of similar samples during decoding and reconstruction, thereby constructing a discrete latent space with explicit semantic structure. In the second stage, an autoregressive Transformer model is trained to learn the category conditional distribution of token sequences within this space. In the enhancement stage, for the target minority categories, the Transformer generates new token sequences, which are then fused with the corresponding category's multi-scale prototypes and broadcast decoded to reconstruct high-fidelity, pathologically semantically consistent synthetic respiratory sound samples. Finally, the synthetic samples are added to the training set to balance the data distribution, effectively improving the downstream respiratory sound classification model, especially its performance and overall robustness in identifying a minority of abnormal categories.

The application provides an in-vivo respiratory sound monitoring method and system, which is suitable for the technical field of medical treatment, and the method is as follows: starting an auxiliary light source and a measuring light source to output first reference light, second reference light and combined light; reflecting the combined light through a respiratory sound collection optical fiber to obtain first reflected light; the respiratory sound collection optical fiber is integrated on a respiratory catheter, and the respiratory sound collection optical fiber is placed into the airway of a patient synchronously with the catheter to construct an in-situ monitoring link of the airway source; and respiratory sound monitoring results are obtained by using the first reference light, the second reference light and the first reflected light, so that the real-time, high-fidelity and low-interference continuous collection of in-vivo respiratory sound signals is realized.

This invention discloses a non-invasive rapid detection and diagnosis system for upper airway function, belonging to the field of upper airway function detection and medical diagnostic technology. It is applicable to upper airway function assessment in patients with mandibular retrusion and non-invasive diagnosis of adenoid hypertrophy in children. The system consists of a three-tiered architecture comprising a multimodal oral-nasal co-measurement instrument, a data acquisition and transmission module, and an intelligent diagnostic module. It integrates multiple types of sensors to simultaneously collect multimodal signals such as oral and nasal airflow, lip status, and neck airway breath sounds. After standardized processing, it achieves accurate grading and diagnosis of upper airway function and adenoid hypertrophy through hierarchical logic including three-dimensional feature extraction, respiratory pattern discrimination, core + auxiliary indicator grading diagnosis, and machine learning model verification. It is also equipped with multiple supporting functional modules and features non-invasiveness, dynamic operation, portability, and high diagnostic accuracy, filling a gap in related technologies.

This application relates to the field of medical technology, specifically to a method and system for managing exercise fatigue. The method synchronously collects heart rate, electromyography, movement, and respiratory sound signals using a multimodal wearable device, extracts characteristic parameters from each signal, performs feature fusion using a weighted average method, predicts fatigue risk types using a time series model, and outputs personalized recovery suggestions accordingly. This application overcomes the shortcomings of traditional single-parameter monitoring methods—poor specificity and inability to comprehensively reflect the user's physical condition—through the synchronous acquisition and fusion analysis of multimodal physiological signals, achieving accurate assessment of complex physiological states. It can predictively assess fatigue status, solving the problems of lag and strong subjectivity in existing subjective assessment methods. Through targeted recovery suggestions, it achieves proactive and targeted physiological regulation intervention, making it particularly suitable for scenarios requiring precise load management, such as cardiopulmonary rehabilitation for the elderly.

The application discloses a pediatric wheezing severity intelligent evaluation method and system based on respiratory sound analysis, relates to the technical field of medical information evaluation, and comprises the following steps: acquiring multi-modal monitoring data composed of respiratory sound audio, chest and abdominal movement signals and blood oxygen saturation; calculating a respiratory compensation index reflecting a physiological compensation state based on the data and dynamically adjustable wheezing sound weight coefficients and chest and abdominal time difference weight coefficients; constructing a multi-dimensional drug response curve by spline interpolation using the index sequence; segmenting the curve and extracting physiological mode features to generate an identification vector; when the length of the identification vector exceeds a threshold value, dynamically optimizing the weight coefficients by a preset maximum value function based on the deviation ratio of each component of the identification vector to the threshold value, the direction similarity and the length of the reference vector; and finally inputting the curve into a pre-trained neural network model to output pediatric response auxiliary evaluation information. The application realizes objective, continuous, quantitative and adaptive evaluation of the treatment response of the patient.

1. The name of the design product: Respiratory sound collector probe (negative pressure). 2. The use of the design product: For adsorbing on the surface of human skin to collect respiratory sound and heartbeat sound. 3. The design points of the design product: In shape. 4. The picture or photo that best indicates the design points: Perspective view 1.

The present application relates to the technical field of respiratory health monitoring, in particular to a respiratory health auxiliary monitoring method and system for chronic lung diseases, comprising: collecting a chest fluctuation image sequence, an environmental sound stream and a wearable chest and abdominal movement waveform to form multi-modal respiratory data, and obtaining a respiratory effort signal, a respiratory sound feature and a chest and abdominal breathing component through targeted processing. Relying on the respiratory physiological coupling relationship, a high-credibility comprehensive respiratory waveform is generated through an asynchronous adaptive fusion algorithm, multi-dimensional features such as respiratory rhythm, depth and symmetry are extracted, a pre-trained chronic disease state classification model is input, and a corresponding disease category and severity grade are output. The method can weaken the time sequence difference of multi-source signals, strengthen the expression of respiratory physiological features, improve the recognition accuracy of abnormal respiratory patterns, and realize continuous and refined respiratory state monitoring and disease grading evaluation for chronic lung diseases.