Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures

Abstract

Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.

Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

“…Hussein et al [35] proposed an approach to augment deep learning models with adversarial training for robust prediction of epilepsy seizures. Though their goal was to overcome some challenges in EEG-based seizure classification, e.g.,…”

“…Application Data Model Adversarial Modification Modification Detection [35] BCI [72] BCI [10] Health Informatics [11] Health Informatics [42] Biometrics individual differences and shortage of pre-ictal labeled data, their approach can also be used to defend against adversarial attacks. They first constructed a deep learning classifier from available limited amount of labeled EEG data, and then performed white-box attacks to the classifier to obtain adversarial examples, which were next combined with the original labeled data to retrain the deep learning classifier.…”

Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

“…Using these techniques, several methods for disease diagnosis have developed and evaluated using public datasets. The classification of these studies based on agile machine learning techniques is an important task to reduce the estimates of various types of diseases, which is the aim of this research [2].…”

RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather

“…In recent studies [18], [19], [20], adversarial training is considered as one of the most promising ways to improve robustness especially for classification problems [6], [21], [24], [25], [26]. Nevertheless, it has not been used in camera localization under domain shift.…”