Abstract: Sleep conditions such as moderate-to-severe sleep apnea can be assessed using a multi-night assessments. A respiration signal (e.g., acquired from a sensor strip) can be processed via a computing device. The respiration signal can be segmented and the segments can be classified to identify one or more apnea/hypopnea events. In some examples, some of the segments can be normalized such that each segment input for classification can be of the same size. The identified one or more apnea/hypopnea events can be used to estimate a nightly parameter indicative of a severity of (or presence of) sleep apnea. The nightly parameters from a multi-night period can be used to estimate a multi-night parameter indicative of the severity of (or presence of) sleep apnea. In some examples, quality checks can be performed to filter out some data (e.g., to exclude data from entire nights or exclude a portion of data from individual nights).

Abstract: A method for event detection in a user-wearable device includes receiving, from a first sensor implemented in the user-wearable device, photoplethysmogram (PPG) signals; processing, at a processor, the PPG signals to obtain PPG signal samples; detecting, at the processor, beats in the PPG signal samples; dividing the PPG signal samples into PPG signal segments; extracting at least one inter-beat interval (IBI) feature in each PPG signal segment; classifying, at the processor, each PPG signal segment using the extracted IBI feature associated with the PPG signal segment and using a machine learning model; in response to the classifying, generating, at the processor, an event prediction result for the PPG signal segment based on the extracted IBI feature; and displaying the event prediction result at the user-wearable device. In another embodiment, the method further includes extracting morphology based features.

Abstract: A method for event detection in a user-wearable device includes receiving, from a first sensor implemented in the user-wearable device, photoplethysmogram (PPG) signals; processing, at a processor, the PPG signals to obtain PPG signal samples; detecting, at the processor, beats in the PPG signal samples; dividing the PPG signal samples into PPG signal segments; extracting at least one inter-beat interval (IBI) feature in each PPG signal segment; classifying, at the processor, each PPG signal segment using the extracted IBI feature associated with the PPG signal segment and using a machine learning model; in response to the classifying, generating, at the processor, an event prediction result for the PPG signal segment based on the extracted IBI feature; and displaying the event prediction result at the user-wearable device. In another embodiment, the method further includes extracting morphology based features.