帕金森病使用单导联心电图的机器学习分型：有或无快速动眼睡眠行为障碍

Aug 6, 2026

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Rapid eye movement sleep behavior disorder is a prodromal marker of alpha-synucleinopathy, and Parkinson’s disease with REM sleep behavior disorder tends to show more severe clinical and cerebral pathology than Parkinson’s disease without it. This retrospective study used single-lead electrocardiogram signals from polysomnography to classify idiopathic REM sleep behavior disorder, Parkinson’s disease with REM sleep behavior disorder, Parkinson’s disease without REM sleep behavior disorder, and controls. Heart-rate-variability features were extracted and machine-learning models distinguished the groups.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Background and Objective

Methods

Results

Conclusions

Introduction

ORIGINAL ARTICLE Sleep Med Res 2025;16(3):174-184 [https://doi.org/10.17241/smr.2025.02831](https://doi.org/10.17241/smr.2025.02831) pISSN 2093-9175 eISSN 2233-8853 Classifying Parkinson’s Disease With or Without Rapid Eye Movement Sleep Behavior Disorder Using Machine Learning-Based Analysis of Single-Lead Electrocardiogram Hye Jeong Lee, MD1, Jonguk Park, PhD2*, Ju HyuckHan, PhD3†, Kyung Min Kim, MD, PhD4, Wonwoo Lee, MD5 1Department of Neurology, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Korea Departments of 2Medical Artificial Intelligence and 3Medical Engineering, Konyang University, Daejeon, Korea 4Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea 5Department of Neurology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea Received: April 8, 2025 Revised: June 4, 2025 Accepted: July 21, 2025 Corresponding Author Wonwoo Lee, MD Department of Neurology, Yongin Severance Hospital, Yonsei University College of Medicine, 363 Dongbaekjukjeon-daero, Giheung-gu, Yongin 16995, Korea Tel +82-31-5189-8176 Fax +82-2-393-0705 E-mail [nawonwoo01@yonsei.ac.kr](nawonwoo01@yonsei.ac.kr) *Current affiliation: Department of Artificial Intelligence, Interdisciplinary College of AI∙SW, Konyang University, Daejeon, Korea †Current affiliation: Departments of Medical Information Technology Engineering, Konyang University, Daejeon, Korea ORCIDiDs Hye Jeong Lee [https://orcid.org/0000-0002-5402-9189](https://orcid.org/0000-0002-5402-9189)[ ](https://orcid.org/0000-0002-5402-9189)Jonguk Park [https://orcid.org/0000-0002-1068-3867](https://orcid.org/0000-0002-1068-3867)[ ](https://orcid.org/0000-0002-1068-3867)Ju HyuckHan [https://orcid.org/0000-0002-6987-2793](https://orcid.org/0000-0002-6987-2793)[ ](https://orcid.org/0000-0002-6987-2793)Kyung Min Kim [https://orcid.org/0000-0002-0261-1687](https://orcid.org/0000-0002-0261-1687)[ ](https://orcid.org/0000-0002-0261-1687)Wonwoo Lee [https://orcid.org/0000-0002-0907-4212](https://orcid.org/0000-0002-0907-4212) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by-nc/4.0](licenses/by-nc/4.0)) [which permits](which permits)[ ](which permits)[unrestricted non-commercial use](unrestricted non-commercial use), distribution, and reproduction in any medium, provided the original work is properly cited. Background and Objective Rapid eye movement sleep behavior disorder is a prodromal stage of alpha-synucleinopathy. Parkinson’s disease with rapid eye movement sleep behavior disorder is associated with more severe symptoms and cerebral pathology compared to Parkinson’s disease without rapid eye movement sleep behavior disorder. This study classified idiopathic rapid eye movement sleep behavior disorder, Parkinson’s disease with rapid eye movement sleep behavior disorder, and Parkinson’s disease without rapid eye movement sleep behavior disorder using single-lead electrocardiogram signals from polysomnography. Methods Subjects who underwent polysomnography and dopamine transporter positron emission tomography between January 2010 and December 2021 were retrospectively analyzed. The study included 4 patients with idiopathic rapid eye movement sleep behavior disorder, 9 with Parkinson’s disease with rapid eye movement sleep behavior disorder, 8 with Parkinson’s disease without rapid eye movement sleep behavior disorder, 9 control subjects, and 15 healthy controls. Heart rate variability features were extracted from electrocardiogram signals, and machine learning models classified the groups. Results No significant differences in demographics or obstructive sleep apnea severity were found between the groups, except for healthy controls. Machine learning classifiers effectively distinguished idiopathic rapid eye

FAQs

研究要解决的核心问题是什么？

研究旨在用单导联心电图信号的机器学习方法，区分：特发性快速动眼睡眠行为障碍、合并快速动眼睡眠行为障碍的帕金森病，以及不合并快速动眼睡眠行为障碍的帕金森病，并为后续预测转归的研究提供依据。

该研究如何获取数据并提取特征？

纳入接受多导睡眠监测和多巴胺转运体正电子发射断层显像的受试者，提取心电图信号的心率变异性特征，然后训练机器学习模型进行分型。

研究的主要结果表明了什么？

除健康对照组外，各组在人口学或阻塞性睡眠呼吸暂停严重程度方面未见显著差异；机器学习分类器能够基于心电图特征有效区分三类相关疾病亚群。

作者的结论对未来研究有什么意义？

研究提示单导联心电图信号具有区分相关疾病亚群的潜力，并提供思路用于未来前瞻性研究，以预测从快速动眼睡眠行为障碍向α-突触核蛋白病的转化。

Aug 6, 2026

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