Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography. (October 2020)

• Record Type: Journal Article
• Authors: Takahashi, Hirokazu; Emami, Ali; Shinozaki, Takashi; Kunii, Naoto; Matsuo, Takeshi; Kawai, Kensuke
• Is Part Of: Computers in biology and medicine. Volume 125(2020)
• Publication Date: 2020-10
• Subjects: Autoencoder -- Convolutional neural network -- Electroencephalography -- Epilepsy -- Seizure -- Video-EEG monitoring; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104016
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still has room for reducing the false-positive alarm rate. Methods: We combined a CNN that processed images of EEG plots with patient-specific autoencoders (AE) of EEG signals to reduce the false alarms during seizure detection. The AE automatically logged abnormalities, i.e., both seizures and artifacts. Based on seizure logs compiled by expert epileptologists and errors made by AE, we constructed a CNN with 3 output classes: seizure, non-seizure-but-abnormal, and non-seizure. The accumulative measure of number of consecutive seizure labels was used to issue a seizure alarm. Results: The second-by-second classification performance of AE-CNN was comparable to that of the original CNN. False-positive seizure labels in AE-CNN were more likely interleaved with "non-seizure-but-abnormal" labels than with true-positive seizure labels. Consequently, "non-seizure-but-abnormal" labels interrupted runs of false-positive seizure labels before triggering an alarm. The median false alarm rate with the AE-CNN was reduced to 0.034 h −1, which was one-fifth of that of the original CNN (0.17 h −1 ). Conclusions: A label of "non-seizure-but-abnormal" offers practical benefits for seizure detection. The modification of a CNN with an AE is worth considering because AEs can automatically assign "non-seizure-but-abnormal" labels in an unsupervised manner with no additional demands on the time of the epileptologist. Highlights: A convolutional neural network (CNN) is useful to process images of EEG plots. CNN and patient-specific autoencoders (AE) were combined to detect seizure. AE was used to make a "non-seizure-but-abnormal" label. The classification performance of AE-CNN was comparable to that of the original CNN. The false seizure alarm rate in AE-CNN was one-fifth of that in the original CNN.