A Novel Independent RNN Approach to Classification of Seizures against Non-seizures
This work addresses the need for accurate and automated seizure detection in clinical EEG analysis, which can reduce labor and errors for neurologists, though it appears incremental as it applies an emerging deep learning model to a known domain-specific challenge.
The paper tackled the problem of automatic seizure/non-seizure classification from EEG data by proposing a new approach using independently recurrent neural networks (IndRNN) to capture variable seizure morphologies, achieving results that outperform current state-of-the-art methods on the CHB-MIT dataset.
In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions. Manual reviews can be laborious and error prone. Automatic and accurate seizure/non-seizure classification methods are desirable. A critical challenge is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure features, this paper leverages an emerging deep learning model, the independently recurrent neural network (IndRNN), to construct a new approach for the seizure/non-seizure classification. This new approach gradually expands the time scales, thereby extracting temporal and spatial features from the local time duration to the entire record. Evaluations are conducted with cross-validation experiments across subjects over the noisy data of CHB-MIT. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art methods. In addition, we explore how the segment length affects the classification performance. Thirteen different segment lengths are assessed, showing that the classification performance varies over the segment lengths, and the maximal fluctuating margin is more than 4%. Thus, the segment length is an important factor influencing the classification performance.