Seizure detection by convolutional neural network-based analysis of scalpelectroencephalography plot images
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- 脳波からてんかんを検出
- 脳波データを時系列処理するのではなく、プロット画像にしてCNNにかけている
- 使っているモデルはVGG16.Chainerのバージョンも1.2と古く、とりあえず深層学習をしてみたと言う雰囲気
- LSTM等の時系列処理モデルとの比較は行われていない
Fig. 1. Seizure detection by image-based CNN of scalp EEG. (A) The flow of seizure detection. The raw EEG was pre-processed with 0.3-Hz high-cut, 60-Hz low-cut, and 50-Hz notch filters. EEG signals were segmented using a given time window (i.e., 0.5 s, 1 s, 2 s, 5 s, and 10 s), and converted to a time series of images. CNN then classified each image into ‘seizure’ or ‘non-seizure’. (B) CNN architecture. VGG-16 model was modified in this study. (C) Testing methods. In leave-one-out testing, a CNN was constructed with EEG data from 23 out of 24 subjects and tested with EEG data from the last remaining subject. In pairwise testing, a CNN was constructed with a single subject's data, and tested with one of other subjects.
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