Department of Emergency Medicine, The Second Hospital of Jiaxing, Jiaxing 314000, China.
Department of Emergency Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou 310009, China.
Comput Math Methods Med. 2022 Feb 15;2022:8724536. doi: 10.1155/2022/8724536. eCollection 2022.
The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios.
精确检测癫痫发作有助于预防癫痫发作的严重后果。由于脑电图（EEG）能有效地反映患者的大脑活动，因此在过去几十年中被广泛用于癫痫发作检测。最近，基于深度学习的检测方法自动从 EEG 信号中学习特征，引起了广泛关注。然而，基于深度学习的检测方法中，EEG 信号的不同输入格式会导致不同的检测性能。在本文中，我们提出了一种基于深度学习的癫痫发作检测方法，该方法采用 EEG 信号的混合输入格式，即原始 EEG、EEG 的傅里叶变换、EEG 的短时傅里叶变换和 EEG 的小波变换。卷积神经网络（CNN）用于从这些输入中提取潜在特征。应用特征融合机制来整合学习到的特征，生成更稳定的综合特征，用于癫痫发作检测。实验结果表明，我们提出的混合方法在小样本场景中有效提高了癫痫发作检测性能。
