EEG-based Neglect Detection for Stroke Patients
EEG-based Neglect Detection for Stroke Patients
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DOI：
10.1109/embc44109.2020.9176378
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发表时间：
2020-07
期刊：
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通讯作者：
Deniz Kocanaogullari;J. Mak;Jessica Kersey;A. Khalaf;S. Ostadabbas;G. Wittenberg;E. Skidmore;M. Akçakaya
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文献类型：
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作者：
Deniz Kocanaogullari;J. Mak;Jessica Kersey;A. Khalaf;S. Ostadabbas;G. Wittenberg;E. Skidmore;M. Akçakaya
空间忽略（SN）是脑卒中患者的一种神经系统综合征，通常由单侧脑损伤引起。它会导致对对侧视野中的刺激不注意。目前SN评估的金标准是行为注意力不集中测试（BIT）。BIT包括一系列纸笔测试。这些测试可能是不可靠的，因为子测试性能的高度可变性;它们在测量忽视程度的能力方面受到限制，并且它们不能在现实和动态的环境中评估患者。在本文中，我们提出了一个基于脑电图（EEG）的脑机接口（BCI），利用星夜测试，以克服传统的SN评估测试的局限性。我们的总体目标是实现这个基于EEG的星夜忽视检测系统是提供一个更详细的评估SN。具体而言，检测SN的存在及其严重程度。为了实现这一目标，作为第一步，我们利用基于卷积神经网络（CNN）的模型来分析EEG数据，并相应地提出了一种疏忽检测方法，以区分脑卒中患者与疏忽患者。临床相关性-所提出的基于EEG的BCI可以用于检测脑卒中患者的疏忽，具有较高的准确性，特异性和灵敏度。进一步的研究还将允许估计患者的视野（FOV），以更详细地评估忽视。
Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inattention test (BIT). BIT includes a series of penand-paper tests. These tests can be unreliable due to high variablility in subtest performances; they are limited in their ability to measure the extent of neglect, and they do not assess the patients in a realistic and dynamic environment. In this paper, we present an electroencephalography (EEG)-based brain-computer interface (BCI) that utilizes the Starry Night Test to overcome the limitations of the traditional SN assessment tests. Our overall goal with the implementation of this EEG-based Starry Night neglect detection system is to provide a more detailed assessment of SN. Specifically, to detect the presence of SN and its severity. To achieve this goal, as an initial step, we utilize a convolutional neural network (CNN) based model to analyze EEG data and accordingly propose a neglect detection method to distinguish between stroke patients without neglect and stroke patients with neglect.Clinical relevance—The proposed EEG-based BCI can be used to detect neglect in stroke patients with high accuracy, specificity and sensitivity. Further research will additionally allow for an estimation of a patient’s field of view (FOV) for more detailed assessment of neglect.