Open AccessOptimizing Seizure Prediction From Reduced Scalp EEG Channels Based on Spectral Features and MAML
Author(s) -
Anibal Romney,
Vidya Manian
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3134166
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Epilepsy is a severe neurological disease with high prevalence and morbidity worldwide. The unpredictability of seizures prevents physicians from tailoring drugs and therapies. Recent non-invasive seizure prediction research has not improved the overall quality of life for patients. Therefore, new research studies on seizure prediction must integrate data, embedded devices, and algorithms. For a seizure prediction system to emerge as a feasible solution, we must address a reduction in EEG scalp electrode channels, along with a decrease in computational resources to train the time-series signal. In this work, we propose an optimized patient-specific channel reduction for seizure prediction using Model Agnostic Meta-Learning (MAML) applied to a Deep Neural Network (DNN). We selected and optimized the number of channels from each of the 23 subjects of the CHB-MIT Dataset. The feature vectors are extracted using Ensemble Empirical Mode Decomposition (EEMD) and Sequential Feature Selection (SFS). We implemented the MAML model to classify the small EEG data generated from the reduced number of subject-dependent electrodes. The experiment results yield an average sensitivity and specificity of 91% and 90%, respectively. Our study demonstrates that MAML is a promising approach to learn EEG patterns to predict epileptic seizures with few EEG scalp electrodes.