AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights From an Apple Watch Study.

Abstract

Polysomnography, the gold standard for sleep evaluations, involves complex setup and data acquisition protocols and requires manual scoring of sleep data. Smartwatches and other multi-sensor consumer wearable devices with automated sleep staging capabilities offer a promising and scalable alternative for routine and long-term sleep evaluations in individuals. We conducted a multi-night study using a smartwatch for sleep assessment and created an AI-driven automated sleep staging framework based on instantaneous heart rate (IHR) and accelerometry data using sleep stage labels based on electroencephalography (EEG) as the reference. 47 healthy adults were recruited to record their sleep for up to seven consecutive nights using an Apple Watch Series 6 and a Dreem 2 Headband. Our sleep staging framework relies on a sequence-to-sequence long short-term memory (LSTM) model with additional convolutional layers. Our model yields a sleep staging accuracy of 71% for classifying every 30-s epoch into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM) sleep. We show through an ablation study that an intra-epoch learning LSTM, incorporation of IHR sampling frequency information, and skip connections from early to late stages of the network are three key architectural advancements that enhance overall sleep staging performance. Our overall contributions include a dedicated Apple Watch app for multi-night raw data acquisition, an open-source library for automated four-class sleep staging, and a public dataset for future investigations.