Scalpnet: Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Using Convolutional Neural Networks
Scalpnet: Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Using Convolutional Neural Networks
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DOI：
10.1109/icassp40776.2020.9054705
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Takahiko Sakai;Takuya Shoji;Noboru Yoshida;Kosuke Fukumori;Yuichi Tanaka;Toshihisa Tanaka
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文献类型：
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作者：
Takahiko Sakai;Takuya Shoji;Noboru Yoshida;Kosuke Fukumori;Yuichi Tanaka;Toshihisa Tanaka
我们提出 ScalpNet：一种深度神经网络，用于检测癫痫患者脑电图的时空异常间隔。由于经过培训的临床医生数量非常有限，因此建立脑电图自动检测癫痫引起的异常信号非常关键。我们构建了一个卷积神经网络来检测异常的时空间隔，因为不仅可以在靠近焦点区域的电极中观察到峰值脑电信号，而且可以在周围区域中观察到峰值脑电信号。在使用真实数据集的实验中，我们提出的 ScalpNet 比现有的机器学习方法（包括逐通道执行的卷积神经网络）表现出更高的分类精度。
We propose ScalpNet: A deep neural network to detect spatiotemporal abnormal intervals from EEGs of epilepsy patients. Since the number of trained clinicians is very limited, it is very crucial to establish automatic detection of abnormal signals caused by epilepsy from EEGs. We build a convolutional neural network detecting spatiotemporal intervals that will be abnormal based on the fact that peaky EEG signals can be observed not only in the electrode close to the focal region but those in the surrounding regions. In the experiments with a real dataset, our proposed ScalpNet presents higher classification accuracy than existing machine learning methods, including a convolutional neural network performed by channel-by-channel.