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Low Latency Automated Epileptic Seizure Detection: Individualized vs. Global Approaches

Seizures significantly reduce the quality of life for epilepsy patients. Short detection latency allows the implementation of a fast response algorithm that can be used for real-time epileptic seizure detection applications. In this paper, we propose an efficient algorithm that extracts time-domain features in the form of one second long EEG recordings, using as input one second long EEG recordings, to ensure reduced computational complexity. The features are fed to a simple neural network for detection. We validate the model, individually and globally on two real-world data sets, namely: Upenn-Mayo Clinic and CHB-MIT. On a per patient basis, results show an accuracy, sensitivity, and specificity of up to 99.17%, 99.44%, and 98.89%, respectively, while globally we reach 92.69%, 91.99%, and 93.38%, respectively.