Generalized sleep decoding with basal ganglia signals in multiple movement disorders
Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged over...
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Published in: | NPJ digital medicine Vol. 7; no. 1; p. 122 |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
London
Nature Publishing Group UK
10-05-2024
Nature Publishing Group Nature Portfolio |
Subjects: | |
Online Access: | Get full text |
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Summary: | Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson’s disease, Essential Tremor, Dystonia, Essential Tremor, Huntington’s disease, and Tourette’s syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder –
BGOOSE
. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances.
BGOOSE
emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2398-6352 2398-6352 |
DOI: | 10.1038/s41746-024-01115-7 |