Sleep Spindle Detection with Soft Dry Electrodes: Toward Clinical and Home-Based Monitoring in Parkinson's Disease.

Sleep spindles, an electroencephalographic (EEG) pattern mostly observed during non-REM sleep in the polysomnography (PSG) lab, are specifically interesting in the context of neurodegeneration. However, their quantification outside the controlled lab setting and in a patient-convenient approach remains challenging. By leveraging a wearable, smart-skin electrophysiology device, we demonstrate the ability to capture sleep spindles at the patient's home and to explore sleep spindle characteristics in healthy controls (HC), patients with Parkinson's disease (PD), and adults at high risk for developing clinical PD. Sleep spindle detection using the wearable system showed a strong correlation with PSG, demonstrating robust signal fidelity. Additionally, sleep spindle characteristics remained highly stable across lab and home settings. Further analysis revealed significant differences in spindle density and slow spindle density among subgroups, with the high-risk group exhibiting a pattern similar to the late-stage PD patients. This study suggests that sleep spindles can be reliably detected using the wearable system in both home and lab settings. Furthermore, significant differences observed in spindle characteristics across groups suggest a relation to prodromal PD. These findings aim to improve the understanding of sleep spindle dynamics in neurodegenerative processes and explore their potential as biomarkers for early diagnosis and monitoring disease progression.Clinical relevance The findings reported in this study support the potential for home-based sleep monitoring using a wearable system capable of capturing EEG patterns, including sleep spindles. This approach could facilitate early diagnosis, risk stratification, and personalized treatment assessment in neurodegenerative diseases, particularly in evaluating sleep quality and cognitive function, ultimately enhancing clinical decision-making and intervention strategies.