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Feature Extraction with Stacked Autoencoders for Epileptic Seizure Detection

Supratak, A, Li, L, Guo, Y (2014) Feature Extraction with Stacked Autoencoders for Epileptic Seizure Detection. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. . pp. 4184-4187. IEEE (doi:10.1109/EMBC.2014.6944546) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:48648)

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Abstract

Scalp electroencephalogram (EEG), a recording of the brain's electrical activity, has been used to diagnose and detect epileptic seizures for a long time. However, most researchers have implemented seizure detectors by manually hand-engineering features from observed EEG data, and used them in seizure detection, which might not scale well to new patterns of seizures. In this paper, we investigate the possibility of utilising unsupervised feature learning, the recent development of deep learning, to automatically learn features from raw, unlabelled EEG data that are representative enough to be used in seizure detection. We develop patient-specific seizure detectors by using stacked autoencoders and logistic classifiers. A two-step training consisting of the greedy layer-wise and the global fine-tuning was used to train our detectors. The evaluation was performed by using labelled dataset from the CHB-MIT database, and the results showed that all of the test seizures were detected with a mean latency of 3.36 seconds, and a low false detection rate.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EMBC.2014.6944546
Uncontrolled keywords: Neural networks in biosignal processing and classification; Biomedical signal classification
Subjects: Q Science > Q Science (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 26 May 2015 12:33 UTC
Last Modified: 17 Aug 2022 10:58 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48648 (The current URI for this page, for reference purposes)
Li, L: https://orcid.org/0000-0002-4026-0216
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