Epileptic seizure prediction based on multiresolution convolutional neural networks

Epilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course daily life. This article pursues development a smart neurocomputational technology to alert epileptic patients wearing EEG sensors an impending seizure. An innovative approach for seizure prediction has been proposed improve accuracy reduce false alarm rate comparison with state-of-the-art benchmarks. Maximal overlap discrete wavelet transform was used decompose signals into different frequency resolutions, multiresolution convolutional neural network is designed extract discriminative features from each band. The algorithm automatically generates patient-specific best classify preictal interictal segments subject. method can be applied any patient case dataset without need handcrafted feature extraction procedure. tested two popular epilepsy datasets. It achieved sensitivity 82% 0.058 Children’s Hospital Boston-MIT scalp 85% 0.19 American Society Seizure Prediction Challenge dataset. provides personalized solution that improved specificity, yet because algorithm’s intrinsic ability generalization, it emancipates reliance on epileptologists’ expertise tune wearable technological aid, which will ultimately help deploy broadly, including medically underserved locations across globe.