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

IF 7.4 1区医学 Q1 CLINICAL NEUROLOGY

Movement Disorders Pub Date : 2024-08-23 DOI:10.1002/mds.29987

Arjun Balachandar MD, Yosra Hashim, Okeanis Vaou MD, Alfonso Fasano MD, PhD, FAAN

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引用次数: 0

Abstract

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.

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利用脑深部刺激和机器学习自动检测运动障碍症患者的睡眠状况。

背景：运动障碍中的自动睡眠检测可用于监测睡眠：运动障碍中的自动睡眠检测可监测睡眠，从而为适应性深部脑刺激（DBS）提供潜在指导：目的：比较家庭环境中唤醒与睡眠状态（WSS）的局部场电位（LFP），并开发帕金森病（PD）、本质性震颤（ET）和抽动秽语综合征（TS）患者 WSS 的生物标记物：5名帕金森病患者、2名ET患者和1名TS患者植入了美敦力Percept（3个STN[丘脑下核]、3个GPi[苍白球内核]和2个腹侧中间核）。在五到七个晚上，记录了β波段（12.5-30赫兹）和/或α波段（7-12赫兹）LFP功率谱密度。可佩戴的行为记录仪跟踪睡眠情况：结果：从睡眠到觉醒，PD LFP β功率在STN中增加，在GPi中减少，α功率在两者中均增加。对机器学习分类器进行了训练。对于帕金森病，WSS的最高准确率为93%（F1 = 0.93），所有患者的准确率为86%（F1 = 0.86）。ET和TS的最高准确率分别为86%和89%：在这项概念验证研究中，慢性颅内窄带记录可准确识别各种运动障碍和目标的睡眠情况。© 2024 The Author(s).运动障碍》由 Wiley Periodicals LLC 代表国际帕金森和运动障碍协会出版。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Movement Disorders 医学-临床神经学

CiteScore

13.30

自引率

8.10%

发文量

371

审稿时长

12 months

期刊介绍： Movement Disorders publishes a variety of content types including Reviews, Viewpoints, Full Length Articles, Historical Reports, Brief Reports, and Letters. The journal considers original manuscripts on topics related to the diagnosis, therapeutics, pharmacology, biochemistry, physiology, etiology, genetics, and epidemiology of movement disorders. Appropriate topics include Parkinsonism, Chorea, Tremors, Dystonia, Myoclonus, Tics, Tardive Dyskinesia, Spasticity, and Ataxia.