Pre-Ictal Seizure Prediction Using Personalized Deep Learning

TL;DR

This study developed a personalized deep learning approach using physiological data to predict epileptic seizures up to two hours in advance, significantly improving accuracy for individual patients.

Contribution

The paper introduces a novel personalized deep learning model that enhances seizure prediction accuracy by tailoring predictions to individual patients using transfer learning.

Findings

General model accuracy: 91.94%

Personalized model accuracy: up to 97%

Personalization improves prediction performance significantly

Abstract

Introduction: Approximately 23 million or 30% of epilepsy patients worldwide suffer from drug-resistant epilepsy (DRE). The unpredictability of seizure occurrences, which causes safety issues as well as social concerns, restrict the lifestyles of DRE patients. Surgical solutions and EEG-based solutions are very expensive, unreliable, invasive or impractical. The goal of this research was to employ improved technologies and methods to epilepsy patient physiological data and predict seizures up to two hours before onset, enabling non-invasive, affordable seizure prediction for DRE patients. Methods: This research used a 1D Convolutional Neural Network-Based Bidirectional Long Short-Term Memory network that was trained on a diverse set of epileptic patient physiological data to predict seizures. Transfer learning was further utilized to personalize and optimize predictions for specific patients. Clinical data was retrospectively obtained for nine epilepsy patients via wearable devices over a period of about three to five days from a prospectively maintained database. The physiological data included 54 seizure occurrences and included heart rate, blood volume pulse, accelerometry, body temperature, and electrodermal activity. Results and Conclusion: A general deep-learning model trained on the physiological data with randomly sampled test data achieved an accuracy of 91.94%. However, such a generalized deep learning model had varied performances on data from unseen patients. When the general model was personalized (further trained) with patient-specific data, the personalized model achieved significantly improved performance with accuracies as high as 97%. This preliminary research shows that patient-specific personalization may be a viable approach to achieve affordable, non-invasive seizure prediction that can improve the quality of life for DRE patients.