SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

• Type of Material: Article
• Authors: Irfan Al-Hussaini, Georgia Institute of Technology; Cassie Mitchell, Emory University
• Language: English
• Date: 2023-08
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.3390%2Fbioengineering10080918
• Title of Journal or Parent Work: Bioengineering
• Volume: 10
• Issue: 8
• Start Page: 918
• Grant/Funding Information: This research was funded by the National Science Foundation CAREER grant 1944247 to C.S.M, the National Institute of Health grant U19-AG056169 sub-award to C.S.M., the Chan Zuckerberg Initiative grant 253558 to C.S.M.
• Keywords: imbalanced classes; artificial intelligence; electroencephalogram; EEG; seizure; xai; machine learning; augmentation; interpretability
• Research Categories: Biology, Neuroscience; Artificial Intelligence

Abstract

This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.