Epileptic seizure detection using novel Multilayer LSTM Discriminant Network and dynamic mode Koopman decomposition

Abstract

Epilepsy is a neurological disorder that causes abnormality in brain function and leads to unusual behavior, awareness loss and other defects. An Electroencephalogram (EEG) is an electrophysiological screening method used for diagnosing epileptic seizure and further brain activities. Usually, physicians inspect the brain abnormalities. But this technique is highly time-consuming, has poor consistency, and has faced difficulty in detecting the seizure due to the imbalanced distribution of data. To overcome these issues a novel seizure predicting algorithm has been proposed. Initially, data is pre-processed using modified common spatial pattern (CSP) which will convert various channels into alternate channels. This in turn results in a maximized signal to noise ratio. For feature extraction, Dynamic Mode Koopman Decomposition (DMKD) and Empirical Mode Decomposition (EMD) has been utilized by obtaining multiple IMFs (Intrinsic Mode Functions). A scheme called Novel Multilayer LSTM Discriminant Network is used for the classification process and is used for efficiently classifying featured vectors. The classification process is used for classifying ictal and non-ictal regions. To avoid misinterpretations and to reduce false alarm rate, post-processing is done. The performance of the proposed system is analyzed using various parameters such as accuracy, specification, sensitivity, Jaccard and other parameters. The proposed is compared with existing methods namely LMS (Least Mean Square) and Wiener in terms of PSNR (Peak Signal-to-Noise Ratio), LMSE (Least Mean Square Error), Contrast-to-Noise Ratio (CNR) and error rates. From performance analysis, it is clear that the proposed framework with an efficient algorithm will provide effective detection for seizure and non-seizure patients.