Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network
This provides an accurate and efficient seizure detection method for pediatric patients, potentially reducing monitoring complexity, but it is incremental as it builds on existing deep learning approaches.
This work tackled seizure detection in EEG data from pediatric patients with absence seizures, achieving a sensitivity of 95.8% and 0.17 false alarms per hour using a deep convolutional neural network (SeizNet) with full channels, and 93.3% sensitivity with 0.58 false alarms using only two channels.
This work aims to develop an end-to-end solution for seizure onset detection. We design the SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with traditional machine learning approach, a baseline classifier is implemented using spectrum band power features with Support Vector Machines (BPsvm). We explore the possibility to use the least number of channels for accurate seizure detection by evaluating SeizNet and BPsvm approaches using all channels and two channels settings respectively. EEG Data is acquired from 29 pediatric patients admitted to KK Woman's and Children's Hospital who were diagnosed as typical absence seizures. We conduct leave-one-out cross validation for all subjects. Using full channel data, BPsvm yields a sensitivity of 86.6\% and 0.84 false alarm (per hour) while SeizNet yields overall sensitivity of 95.8 \% with 0.17 false alarm. More interestingly, two channels seizNet outperforms full channel BPsvm with a sensitivity of 93.3\% and 0.58 false alarm. We further investigate interpretability of SeizNet by decoding the filters learned along convolutional layers. Seizure-like characteristics can be clearly observed in the filters from third and forth convolutional layers.