1,479 research outputs found
Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus:A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field.</p
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection
Wearable devices for seizure monitoring detection could significantly improve
the quality of life of epileptic patients. However, existing solutions that
mostly rely on full electrode set of electroencephalogram (EEG) measurements
could be inconvenient for every day use. In this paper, we propose a novel
knowledge distillation approach to transfer the knowledge from a sophisticated
seizure detector (called the teacher) trained on data from the full set of
electrodes to learn new detectors (called the student). They are both providing
lightweight implementations and significantly reducing the number of electrodes
needed for recording the EEG. We consider the case where the teacher and the
student seizure detectors are graph neural networks (GNN), since these
architectures actively use the connectivity information. We consider two cases
(a) when a single student is learnt for all the patients using preselected
channels; and (b) when personalized students are learnt for every individual
patient, with personalized channel selection using a Gumbelsoftmax approach.
Our experiments on the publicly available Temple University Hospital EEG
Seizure Data Corpus (TUSZ) show that both knowledge-distillation and
personalization play significant roles in improving performance of seizure
detection, particularly for patients with scarce EEG data. We observe that
using as few as two channels, we are able to obtain competitive seizure
detection performance. This, in turn, shows the potential of our approach in
more realistic scenario of wearable devices for personalized monitoring of
seizures, even with few recordings
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