Research Area:  Machine Learning

Abstract:

Till date, according to the World Health Organization (WHO), more than 50 million people around the globe are suffering from epilepsy. Epilepsy is a neurological disorder characterized by the onset of intractable seizures. Seizures are the aberrant behaviour of cerebral signals which leaves the patient debilitated. Electroencephalogram (EEG) which measures the brain wave activity and neuro-imaging like CT scan and MRI are usually used for diagnosing epilepsy. Despite the fact that around 12 million Indian citizens suffer from chronic disease, there still exists a huge stigma associated with epilepsy. Social stigma so grave, that in India, there are a considerable number of marriages which are annulled or called off because either of the partners suffers from epilepsy. Compared to a healthy human being, the chances of survival for a person with epilepsy are 1.6–3 times lower. Hence, it calls for more attention to the detection of epilepsy. This paper gives the comparative study of different methodologies implemented so far for epilepsy detection and identifies the existing gaps in these methods.

Keywords:  

Author(s) Name:  Ananya D. Ojha, Ananya Navelkar, Madhura Gore & Dhananjay Kalbande

Publisher name:  Springer

DOI:  10.1007/978-981-15-1286-5_18

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-981-15-1286-5_18