Unveiling Intractable Epileptogenic Brain Networks with Deep Learning
  Algorithms: A Novel and Comprehensive Framework for Scalable Seizure
  Prediction with Unimodal Neuroimaging Data in Pediatric Patients
Bliss Singhal Fnu Pooja
Epilepsy is a prevalent neurological disorder affecting 50 million
individuals worldwide and 1.2 million Americans. There exist millions of
pediatric patients with intractable epilepsy, a condition in which seizures
fail to come under control. The occurrence of seizures can result in physical
injury, disorientation, unconsciousness, and additional symptoms that could
impede children's ability to participate in everyday tasks. Predicting seizures
can help parents and healthcare providers take precautions, prevent risky
situations, and mentally prepare children to minimize anxiety and nervousness
associated with the uncertainty of a seizure. This research proposes a novel
and comprehensive framework to predict seizures in pediatric patients by
evaluating machine learning algorithms on unimodal neuroimaging data consisting
of electroencephalogram signals. The bandpass filtering and independent
component analysis proved to be effective in reducing the noise and artifacts
from the dataset. Various machine learning algorithms' performance is evaluated
on important metrics such as accuracy, precision, specificity, sensitivity, F1
score and MCC. The results show that the deep learning algorithms are more
successful in predicting seizures than logistic Regression, and k nearest
neighbors. The recurrent neural network (RNN) gave the highest precision and F1
Score, long short-term memory (LSTM) outperformed RNN in accuracy and
convolutional neural network (CNN) resulted in the highest Specificity. This
research has significant implications for healthcare providers in proactively
managing seizure occurrence in pediatric patients, potentially transforming
clinical practices, and improving pediatric care.