Abstract: Apparatus and methods, such as with processing apparatus (102), distinguish sleep disordered breathing (SDB) events from a signal generated by, for example, a photoplethysmography (PPG) sensor. Data representing the signal from the sensor may be received and a PPG-based respiratory signal may be derived from data. First and/or second level features may be computed from the respiratory signal. The first and/or second level features may be evaluated, such as in a classifier. Based on the evaluation, an output indication identifying SDB type may be generated. In some implementations, the apparatus, detects SDB event(s) from a PPG signal. Data representing the signal from the PPG sensor may be received. A vasoconstriction event and/or arrhythmia event may be detected from the data. The data representing the PPG signal may be evaluated to generate, based on the evaluation and the detected events of vasoconstriction and/or arrhythmias, an output indication identifying SDB event(s).

Abstract: A system is configured to diagnose sleep comprising an apparatus configured to be attached to a patient; the apparatus comprises: i) an optical sensor configured to measure a blood volume pulse of the patient; and ii) a wireless communication interface configured to wirelessly transmit the measured blood volume pulse. The system further comprises means for performing: i) obtaining the wirelessly transmitted measured blood volume pulse; and ii) deriving from the blood volume pulse the peripheral arterial tone; iii) determining occurrences of sleep events from changes in the peripheral arterial tone.

Abstract: Apparatus and methods detect sleep disordering events. The apparatus may be configured to access one or more physiological signals generated by one or more sensors. The apparatus may be configured to detect, from the one or more physiological signals, seed events suggestive of sleep disordering events. The apparatus may be configured to compute features indicative of patterns within portions of the one or more physiological signals that are associated with the detected seed events. The apparatus may be configured to apply to a classifier, the computed features indicative of patterns of the seed events. The classifier may be trained to compute a degree of fit of the computed features to learned repetitive patterns of sleep disordering events. The apparatus may be configured to output an identification of sleep disordering event(s) corresponding with the seed events based on the computed degree of fit determined by the classifier.

Abstract: A method is disclosed (100) for obtaining respiratory related sounds (160), RRSs, originating from a target patient, the method comprising the steps of obtaining an input audio recording (110, 111) of a sleeping environment of the target patient, obtaining a respiratory trace (150) of the target patient's respiration, identifying (120) RRSs (130) in the input audio recording, and selecting (140), based on the respiratory trace, from the RRSs, the RRSs (160) originating from the target patient. The selecting further comprises: determining a first and/or second subset of the RRSs having a respective high and/or low probability of originating from the target patient, training a classifier based on the first and/or a second subset to select RRSs originating from the target patient, and selecting the RRSs originating from the target patient (160) by the trained classifier.

Abstract: The present disclosure relates to a method and apparatus for detecting sleep-disturbing events from a signal indicative of a peripheral arterial tone of an individual, the signal being affected by a venoarteriolar reflex, wherein sleep-disturbing events are detected by: determining a vasoconstriction event from changes in the signal; deriving, a reference amplitude value and a baseline amplitude value for the vasoconstriction event, the reference amplitude value being different from the baseline amplitude value; relating the reference amplitude value for the vasoconstriction event to the baseline amplitude value for the vasoconstriction event, thereby obtaining a magnitude measure for the vasoconstriction event; and detecting, therefrom, a sleep-disturbing event.

Abstract: A computer-implemented method for assessing peripheral arterial tone (100), PAT, of an individual (1) monitored by optical plethysmography and for detecting therefrom occurrence of a sleep-related event, comprising:—obtaining: o an optical plethysmography signal (101) measured at an investigated volume (11); and o light intensities (102;103) acquired at two or more points in time (12;13) along said optical plethysmography signal (101);—determining changes in arterial blood volume (16) in said investigated volume (11) between said two or more points in time (12;13) by determining a logarithm (17) or a function approximation thereof (17) of a function of said light intensities (102;103), thereby assessing PAT (100) of said individual (1); and—determining a drop in said PAT (100) indicative for a vasoconstriction of arteries and arterioles in said investigated volume (11), thereby detecting occurrence of a sleep-related event.