Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparation, segmentation, encoding strategies, and performance metrics, results are hard to compare, which makes building upon that knowledge difficult. Thus, the main goal of this work is to perform a systematic assessment of the HD computing framework for the detection of epileptic seizures, comparing different feature approaches mapped to HD vectors. More precisely, we test two previously implemented features as well as several novel approaches with HD computing on epileptic seizure detection. We evaluate them in a comparable way, i.e., with the same preprocessing setup, and with the identical performance measures. We use two different datasets in order to assess the generalizability of our conclusions. The systematic assessment involved three primary aspects relevant for potential wearable implementations: 1) detection performance, 2) memory requirements, and 3) computational complexity. Our analysis shows a significant difference in detection performance between approaches, but also that the ones with the highest performance might not be ideal for wearable applications due to their high memory or computational requirements. Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Dionisije Sopic

In this thesis, I focus on monitoring of patients suffering from cardiovascular and neurological diseases through the use of wearable devices. The main diseases considered in this thesis are atrial fibrillation (AF), myocardial infarction (MI), and epilepsy. The proposed methods for the detection of the considered cardiovascular diseases use ECG as the main biosignal, whereas the main biosignal used for detecting epileptic seizures is EEG. Firstly, I propose a hierarchical heart-rhythm classification method for AF detection from a single lead ECG recording. The proposed method is based on the features that capture the morphology of important ECG signal segments, along with heart-rate oscillations and time and frequency domain features of the ECG signal. Furthermore, the classification scheme used in this method incorporates two different classifiers: a multiclass classifier and a random forest classifier, resulting in an F1 score of 78.95% for AF detection. Secondly, I address the problem of early detection and prevention of MI by introducing a real-time event-driven classification technique that uses a hierarchical classifier with multiple levels. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. The reduction in computational complexity, in turn, results in a longer battery life, which is an important factor for wearable devices. The proposed technique is validated on a public database and ported on a real-life wearable device. The experimental evaluation of the proposed technique on MI data shows that this scheme reduces the energy consumption by a factor of 2.60, with no significant loss in classification performance. The third chapter of this thesis is split into two parts and it focuses on epilepsy. In the first part, I present a real-time method for personalized epileptic seizure detection that uses EEG signals acquired from two electrode pairs: F7T3, and F8T4. The proposed method reaches a sensitivity of 90.98% and specificity of 92.10% on the database used in this study. Furthermore, this method is ported on a pair of eyeglasses in which the used electrodes are embedded and hidden in the temples, allowing for 2.71 days of operation on a single battery charge. This method overcomes the lack of portability and the effect of social stigma of EEG caps, which are used as a gold standard technique for epilepsy detection. Since the main pitfall of epilepsy detection algorithms is the unacceptably high number of false alarms, in the last part of my thesis, I propose an interpretable patient-specific approach to false alarm reduction for epilepsy detection. This approach is based on similarly occurring morphological EEG signal patterns (seizure signature) that occur frequently during seizures. The proposed approach has been experimentally validated on more than 5500 hours of long-term EEG recordings, resulting in a high classification performance with no false positive alarms. The high degree of interpretability of this method can help physicians discover seizures faster, as well as it can be used to improve data labeling quality in publicly available databases, which confirms its applicability for long-term seizure monitoring.

EPFL2020

Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection

David Atienza Alonso, Una Pale, Tomas Teijeiro Campo

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdimensional (HD) computing has evolved in recent years as a new promising machine learning approach, especially when talking about wearable applications. But in the case of epilepsy detection, standard HD computing is not performing at the level of other state-of-the-art algorithms. This could be due to the inherent complexity of the seizures and their signatures in different biosignals, such as the electroencephalogram (EEG), the highly personalized nature, and the disbalance of seizure and non-seizure instances. In the literature, different strategies for improved learning of HD computing have been proposed, such as iterative (multi-pass) learning, multi-centroid learning and learning with sample weight ("OnlineHD"). Yet, most of them have not been tested on the challenging task of epileptic seizure detection, and it stays unclear whether they can increase the HD computing performance to the level of the current state-of-the-art algorithms for wearable devices, such as random forests. Thus, in this paper, we implement different learning strategies and assess their performance on an individual basis, or in combination, regarding detection performance and memory and computational requirements. Results show that the best-performing algorithm, which is a combination of multi-centroid and multi-pass, can indeed reach the performance of the random forest model on a highly unbalanced dataset imitating a real-life epileptic seizure detection application.

2022

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Dionisije Sopic

EPFL2020