The present invention relates to a system and method for determining sleep quality parameters according to audio analyses, comprising: obtaining an audio recorded signal comprising sleep sounds of a subject; segmenting the signal into epochs; generating a feature vector for each epoch, wherein each of said feature vectors comprises one or more feature parameters that are associated with a particular characteristic of the signal and that are calculated according to the epoch signal or according to a signal generated from the epoch signal; inputting the generated feature vectors into a machine learning classifier and applying a preformed classifying model on the feature vectors that outputs a probabilities vector for each epoch, wherein each of the probabilities vectors comprises the probabilities of the epoch being each of the sleep quality parameters; inputting the probabilities vectors for each epoch into a machine learning time series model and applying a preformed sleep quality time series pattern function on said probabilities vectors that outputs an enhanced probabilities vector for each epoch; determining a final sleep quality parameter for each epoch by calculating the most probable sleep quality parameters sequence.

A system is provided for monitoring patients with dysphagia, or swallowing impairments, that monitors muscle movement during intensive swallowing exercises to provide adjuvant visual feedback from surface electromyography.

A lung-sound signal processing method, device and computer-readable storage medium. The lung-sound processing method includes: obtaining a lung sound signal; decomposing the lung sound signal, to obtain a crackle-sound component and a respiration-sound component; obtaining an inspiratory-phase period based on the respiration-sound component; extracting a crackle-sound signal located in the inspiratory-phase period from the crackle-sound component; and obtaining at least one feature parameter according to the crackle-sound signal.

Systems and methods to predict a patient mortality risk are disclosed, including determining a risk value for a plurality of physiologic measures and determining a mortality risk metric indicative of a risk of patient mortality as a weighted combination of the plurality of physiologic measures having risk values satisfying a pre-determined condition.

Systems and methods to predict a patient mortality risk are disclosed, including determining a risk value for a plurality of physiologic measures and determining a mortality risk metric indicative of a risk of patient mortality as a weighted combination of the plurality of physiologic measures having risk values satisfying a pre-determined condition.

A device for detecting stenosis comprising disposable components to ensure function and sanitary conditions, said device having a disposable sensing pad, a disposable piezo assembly, and a disposable sensing pod, wherein the entire device can be disposed of after a predetermined number of uses to ensure accuracy of results and of sanitary conditions.

A device for detecting stenosis comprising disposable components to ensure function and sanitary conditions, said device having a disposable sensing pad, a disposable piezo assembly, and a disposable sensing pod, wherein the entire device can be disposed of after a predetermined number of uses to ensure accuracy of results and of sanitary conditions.

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.

A stethoscope system comprising: a plurality of sensors for collecting auscultatory sounds; and a controller which detects a first heart sound and a second heart sound of heart sounds based on a volume of the auscultatory sounds which are collected by the plurality of sensors.

A digital stethoscope with additional sensors for measuring orientation, positional changes, infrared radiation, strain, and electrophysiology is disclosed. Enhancements to AI driven system(s) and methods as a result of the additional sensors are disclosed. Methods and device implementations enabled by this feature set include providing instruction to a user for improved use of the digital stethoscope in obtaining sound from the body of a patient, automatic exam segmentation, automatic subject orientation capture, and motion capture not possible with a traditional stethoscope transducer alone. A further disclosure of a method for managing heart failure is described.