A Self-Learning Methodology for Epileptic Seizure Detection with Minimally-Supervised Edge Labeling

Abstract

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortality rate among epileptic patients is 2-3 times higher than in the matching group of the general population. Wearable devices offer a promising solution for the detection of seizures in real time so as to alert family and caregivers to provide immediate assistance to the patient. However, in order for the detection system to be reliable, a considerable amount of labeled data is needed to train it. Labeling epilepsy data is a costly and time-consuming process that requires manual inspection and annotation of electroencephalogram (EEG) recordings by medical experts. In this paper, we present a self-learning methodology for epileptic seizure detection without medical supervision. We propose a minimally-supervised algorithm for automatic labeling of seizures in order to generate personalized training data. We demonstrate that the median deviation of the labels from the ground truth is only 10.1 seconds or, equivalently, less than 1% of the signal length. Moreover, we show that training a real-time detection algorithm with data labeled by our algorithm produces a degradation of less than 2.5% in comparison to training it with data labeled by medical experts. We evaluated our methodology on a wearable platform and achieved a lifetime of 2.59 days on a single battery charge.

EpilepsyGAN: Synthetic Epileptic Brain Activities With Privacy Preservation

“…The nonlinear features extracted are: sixth and seventh level sample entropy [54] for k = 0.2 and k = 0.35; third, fourth, fifth, sixth and seventh level permutation entropy [55] for n = 3, n = 5 and n = 7; third, fourth, fifth, sixth and seventh level, as well as raw signal, Shannon, Renyi and Tsallis entropies. The power features are: total power, total and relative band power in the bands delta [0.5,4] Hz, theta [4,8] Hz, alpha [8,12] Hz, beta [13,30] Hz, gamma [30,45] Hz as well as in the bands [0,0.1] Hz, [0.1,0.5] Hz, [12,13] Hz. After the features are extracted, the target training set is used to train the random forest classifier and the resulting classifier is evaluated against the test set.…”

Noninvasive detection of focal seizures in ambulatory patients

“…67 Cutting edge self-learning algorithms, such as generative adversarial networks, which proved highly effective for image processing, might also carry significant progress in FS detection and forecasting. 68 Such approaches would benefit from the next generation of ultra-low-power multicore platforms with embedded machine-learning accelerators, which can offer many advantages in terms of parallelization capabilities to execute complex algorithms and process multimodal data inputs in complex real-life wearable setups. 69 The ideal solution delineated above is not yet available and will require some years to be developed, tested, and validated.…”

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

“…In [47], the authors consider a combination of EEG and ECG monitoring to improve the results. To make collection and labeling of personalized EEG data easier in [48] a self-learning algorithm is used on new data.…”