DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2024-09-10 DOI:10.1088/1741-2552/ad7904
Sha Zhao,Yue Cao,Wei Yang,Jie Yu,Chuan Xu,Wei Dai,Shijian Li,Gang Pan,Benyan Luo
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Abstract

OBJECTIVE Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: 1) the EEG signals have not been fully used; and 2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework. APPROACH We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases. MAIN RESULTS Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics. SIGNIFICANCE The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.
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DOCTer:基于脑电图的新型意识障碍诊断框架。
目的准确诊断意识障碍(DOC)患者具有挑战性且容易出错。最近的研究表明,脑电图(EEG)是一种记录大脑自发电活动的非侵入性技术,可为意识障碍诊断提供有价值的见解。然而,一些挑战依然存在:1)脑电信号尚未得到充分利用;2)大多数现有研究的数据规模有限。在本研究中,我们的目标是通过提出一种新的深度学习框架,利用静息态脑电信号区分微意识状态(MCS)和无反应清醒综合征(UWS)。它能从原始脑电信号中提取多种相关特征,包括时频特征和微状态。同时,它还考虑了患者的临床特征,然后将所有特征结合在一起进行诊断。为了评估其有效性,我们收集了一个大型数据集,其中包含来自 128 个 UWS 和 187 个 MCS 病例的 409 个静息态脑电记录。时间/光谱特征对诊断任务的贡献最大。大脑的完整性对检测意识水平非常重要。同时,我们还研究了不同脑电图采集时间和通道数的影响,以帮助临床做出适当的选择。 意义DOCTer 框架显著提高了 DOC 诊断的准确性,有助于制定适当的治疗方案。大规模数据集的研究结果为临床提供了宝贵的见解。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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