1076 Feasibility of Sleep Staging EEG Acquired by a Dry-electrode Wearable Device Using a PSG-trained Algorithm

Abstract Introduction Sleep monitoring hardware that allows for accurate sleep staging while also being unobtrusive and self-administered has the potential to make reliable EEG-based assessment of sleep quality at home widely accessible. However, for novel hardware utilizing dry EEG electrodes, a question remains regarding the similarity of these signals to traditional polysomnography (PSG), especially when used for therapy development. Here, we investigate how well EEG signals from a wearable dry electrode system can be sleep staged by an algorithm trained on traditional PSG. Methods The Dreem 3 headband, a low-profile sleep monitoring device with dry electrode EEG sensors, was used simultaneously with overnight PSG in 75 individuals. A machine learning model that was trained to stage sleep from the EEG signals of PSG, SleepStageML™, was run without modification on both the PSG and headband EEG signals. Each PSG recording was independently staged by 3 registered polysomnographic technologists to generate consensus ground-truth sleep stage labels. We compared the algorithmic performance of the sleep stages generated from the PSG signals to that of the sleep stages generated from the synchronously recorded headband data. Results Staging of PSG signals resulted in per-stage positive-percent-agreements (PPAs) of 92.4% for W, 58.4% for N1, 90.7% for N2, 76.0% for N3, and 92.9% for R. Negative-percent-agreements (NPAs) for these stages were 98.6%, 98.1%, 88.4%, 97.5%, and 98.4% respectively. When staging the headband recordings, performance remained high with PPAs for W, N1, N2, N3, and R of 94.4%, 34.7%, 88.2%, 78.6%, and 83.7% and NPAs of 96.2%, 98.9%, 86.5%, 96.3%, and 98.4% respectively. Cohen's Kappa on PSG signals was 0.82, and 0.77 on headband data. Conclusion We demonstrate that the EEG signals from a dry-electrode sleep monitoring system are similar enough to the EEG signals of a traditional PSG that an algorithm trained to stage PSG can also stage these dry-electrode signals. These results suggest that dry electrodes configured in a comfortable headband montage can capture clinical grade EEG useful for sleep staging with existing paradigms. These findings are promising for the broader investigation of cerebral functioning in sleep pathology, and could simplify the development of sleep biomarkers of neuropsychiatric disease. Support (if any)