Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning.

BACKGROUND

Automated sleep detection in movement disorders may allow monitoring sleep, potentially guiding adaptive deep brain stimulation (DBS).

OBJECTIVES

The aims were to compare wake-versus-sleep status (WSS) local field potentials (LFP) in a home environment and develop biomarkers of WSS in Parkinson's disease (PD), essential tremor (ET), and Tourette's syndrome (TS) patients.

METHODS

Five PD, 2 ET, and 1 TS patient were implanted with Medtronic Percept (3 STN [subthalamic nucleus], 3 GPi [globus pallidus interna], and 2 ventral intermediate nucleus). Over five to seven nights, β-band (12.5-30 Hz) and/or α-band (7-12 Hz) LFP power spectral densities were recorded. Wearable actigraphs tracked sleep.

RESULTS

From sleep to wake, PD LFP β-power increased in STN and decreased in GPi, and α-power increased in both. Machine learning classifiers were trained. For PD, the highest WSS accuracy was 93% (F1 = 0.93), 86% across all patients (F1 = 0.86). The maximum accuracy was 86% for ET and 89% for TS.

CONCLUSION

Chronic intracranial narrowband recordings can accurately identify sleep in various movement disorders and targets in this proof-of-concept study. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.