Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network

Abstract

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.

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• A systematic study of the class imbalance problem in convolutional neural networks
• Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller

Cited by in corpus (5)

• Epileptic Seizures Detection Using Deep Learning Techniques: A Review
• An End-to-End Deep Learning Approach for Epileptic Seizure Prediction
• Convolutional neural network for detection and classification of seizures in clinical data
• Open and free EEG datasets for epilepsy diagnosis
• Towards Early Diagnosis of Epilepsy from EEG Data