Abstract
Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 20 years. Electroencephalograms have been integral to these studies, as they can capture the brain’s electrical signals. The challenge is to generalise the detection of seizures in different regions of the brain and across multiple subjects. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 543 electroencephalogram segments. Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier.
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Fergus, P., Hignett, D., Hussain, A.J., Al-Jumeily, D. (2014). An Advanced Machine Learning Approach to Generalised Epileptic Seizure Detection. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_14
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DOI: https://doi.org/10.1007/978-3-319-09330-7_14
Publisher Name: Springer, Cham
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