Energy-efficient seizure detection for wearable EEG

Abstract

With the development of machine learning techniques, more and more classification models have been designed for seizure detection. The creation of these models has dramatically improved the convenience of epilepsy detection and made seizure labeling automation possible. However, many of the current researches in this field use EEG datasets with small data volumes and are mainly designed for scientific purposes, which do not have a good performance of actual medical data. Besides, most models require complex time-frequency domain transformation and feature extraction process, which result in low classification speed and makes it difficult to achieve real-time monitoring. Moreover, the excessive complexity also means higher power consumption, so most of these models cannot be implemented with wearable EEG devices.
This thesis proposed a new seizure detection algorithm based on the bidirectional long short-term memory(BiLSTM) technique. The seizure detection function is achieved using time-domain features and LSTM networks. The preprocessing steps of this model are simple, and the complexity is low. Thus its operation speed is significantly improved compared to other traditional models. Also, this model is developed and tested based on TUH EEG corpus, which is an open-access dataset. Therefore, the results are directly comparable to others in the literature.