Microstate feature fusion for distinguishing AD from MCI.

IF 4.7 3区医学 Q1 MEDICAL INFORMATICS

Health Information Science and Systems Pub Date : 2022-07-26 eCollection Date: 2022-12-01 DOI:10.1007/s13755-022-00186-8

Yupan Shi, Qinying Ma, Chunyu Feng, Mingwei Wang, Hualong Wang, Bing Li, Jiyu Fang, Shaochen Ma, Xin Guo, Tongliang Li

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引用次数: 3

Abstract

Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of TPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of TPs were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (TTPs) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.

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微状态特征融合识别AD与MCI。

脑电图微态以其丰富的时间信息为识别脑电图特征提供了有力的工具。在本研究中，我们测试了微状态是否可以衡量阿尔茨海默病(AD)和轻度认知障碍(MCI)患者的严重程度，并有效区分AD和MCI。我们使用转移概率(TPs)定义了两个特征，其中一个用于评估组间微观状态参数的差异，以评估TPs和MMSE评分在组内的一致性。在机器学习模型中，另一个特征被用来区分AD和MCI。结果表明，微状态时间特征组间存在差异，部分TPs与组内MMSE得分存在显著相关。基于我们新定义的时间因子转移概率(TTPs)特征和部分积累策略，我们获得了准确度、灵敏度和特异性的评分，分别为0.938、0.923和0.947。这些结果为微状态作为阿尔茨海默病的神经生物学标志物提供了证据。

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

Health Information Science and Systems MEDICAL INFORMATICS-

CiteScore

11.30

自引率

5.00%

发文量

期刊介绍： Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.