A system having a scope with a longitudinal length extending between a proximal end and a distal end includes a plurality of markers spaced along the longitudinal length. The system also includes a disassociation and positioning device that is configured to enhance unsedated transnasal endoscopic procedures by at least partially occluding the vision of a patient while enabling body cavity access, and optionally record and sense body functions such as temperature, heart rate and oxygenation of the blood stream. The system further includes a sensor integrated into the distraction device, wherein the sensor is configured to detect the markers on the longitudinal length of the scope.

A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

This document discusses, among other things, systems and methods to produce a composite heart sound signal of a patient using a first signal including heart sound information over a first physiologic interval and a second signal including heart sound information over the first physiologic interval.

A computer-implemented method for determining lung pathology from an audio respiratory signal comprises inputting a plurality of audio files comprising a training set into an artificial neural network (ANN), wherein the plurality of audio files comprise sessions with patients with known pathologies of known degrees of severity. The method further comprises annotating the plurality of audio files with metadata relevant to the patients and the known pathologies and analyzing the plurality of audio files, wherein the analyzing comprises extracting spectrograms for each of the plurality of audio files and a plurality of descriptors associated with wheeze and crackle from the plurality of audio files. Additionally, the method comprises training the ANN using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors. The method finally comprises determining a lung pathology associated with a new sound recording inputted into the ANN.

An ostomy bag can include one or more sensors for measuring one or more metrics. An ostomy wafer can also include one or more sensors for measuring one or more metrics. The sensors can be temperature sensors and/or capacitive sensors, for example, and the metrics can include bag fill, leakage, skin irritation, and phase of stoma output, among others.

A method for identifying segments of a digital audio recording of sounds from a subject, where the segments contain particular sound events of interest, the method comprising: filtering the digital audio recording based on a characteristic frequency range of the sound events to produce a filtered digital audio signal; processing the filtered digital audio signal to produce a corresponding signal envelope; fitting a statistical distribution to the signal envelope; determining a threshold level for the signal envelope based on the statistical distribution and a predetermined probability level; and identifying segments of the signal envelope that are above the threshold level to thereby identify corresponding segments of the digital audio recording of sounds from the subject as segments of the digital audio recording containing the particular sound events of interest.

A computer-implemented method, a computer system, and a non-transitory computer readable medium are provided that perform a method for determining an auscultation quality metric (AQM). The computer-implemented method includes obtaining an acoustic signal representative of pulmonary sounds from a patient; determining a plurality of derived signals from the acoustic signal; performing a regression analysis on the plurality of derived signals; and determining the AQM from the regression analysis.

A monitoring apparatus for a human body includes a node network with at least one motion sensor and at least one acoustic sensor. A processor is coupled to the node network, and receives motion information and acoustic information from the node network. The processor determines from the motion information and the acoustic information a source of acoustic emissions within the human body by analyzing the acoustic information in the time domain to identify an event envelope representing an acoustic event, determining a feature vector related to the event envelope, calculating a distance between the feature vector and each of a set of predetermined event silhouettes, and identifying one of the predetermined event silhouettes for which the distance is a minimum.

A medical device system and associated method predict a patient response to a cardiac therapy. The system includes for delivering cardiac pacing pulses to a patient's heart coupled to a cardiac sensing module and a cardiac pacing module for generating cardiac pacing pulses and controlling delivery of the pacing pulses at multiple pace parameter settings. An acoustical sensor obtains heart sound signals. A processor is enabled to receive the heart sound signals, derive a plurality of heart sound signal parameters from the heart sound signals, and determine a trend of each of the plurality of heart sound signal parameters with respect to the plurality of pace parameter settings. An external display is configured to present the trend of at least one heart sound parameter with respect to the plurality of pace parameter settings.