基于变分模态分解的深度学习筛选帕金森病患者轻度认知障碍

Madan Parajuli, A. Amara, M. Shaban
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引用次数: 0

摘要

帕金森病(PD)是美国第二常见的神经退行性疾病,对专家来说,诊断和分级是一个挑战。在PD的运动症状出现之前,患者表现出睡眠结构的改变,这在巩固记忆(大脑的一个关键认知过程)中起着关键作用。标准的频谱和信号分析技术最近被引入,以利用与PD或其认知并发症(包括痴呆)相关的睡眠脑电图的变化。然而,在睡眠脑电图中使用人工智能自动检测PD向轻度认知障碍(MCI)或痴呆的进展尚未进行研究。在本文中,我们引入了一种新的高精度的基于变分模式分解的深度学习框架,用于睡眠脑电图信号,以将PD受试者分为正常认知(NC)或MCI患者。该框架能够以4倍交叉验证的准确性、灵敏度、特异性和近99%的二次加权Kappa评分检测MCI,为专家监测PD的进展提供了快速和支持性的工具,并确保早期开始有效的治疗,从而提高患者及其护理人员的生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Screening of Mild Cognitive Impairment in Patients with Parkinson's Disease Using a Variational Mode Decomposition Based Deep-Learning
Parkinson's disease (PD) which is the second most common neurodegenerative disease in the United States is challenging for specialists to diagnose and grade. Prior to the onset of motor symptoms of PD, patients exhibit alteration in sleep architecture which plays a critical role in consolidating memory, a key cognitive process of the brain. Standard spectral and signal analysis techniques have been recently introduced to exploit the changes in the electroencephalography of sleep related to PD or its cognitive complications including dementia. However, the use of artificial intelligence for the automated detection of the progression of PD to mild cognitive impairment (MCI) or dementia in sleep EEG have not yet been investigated. In this paper, we introduce a novel highly accurate variational mode decomposition based deep-learning framework applied on sleep electroencephalography signals in order to classify PD subjects into patients exhibiting normal cognition (NC) or MCI. The proposed framework is capable of detecting MCI at a significantly high 4-fold cross validation accuracy, sensitivity, specificity and quadratic weighted Kappa score of almost 99% offering a rapid and supportive tool for specialists to monitor the progression of PD and ensure the early initiation of efficient therapeutic treatments that will accordingly improve the quality of life for patients and their caregivers.
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