neurology-neuropsychology-drug-delivery-2019-tracks

Volume 3

Journal of Neurology and Clinical Neuroscience

Neurology 2019 | Neuropsychology 2019 | Drug Delivery Summit 2019

June 24-25, 2019

Page 21

June 24-25, 2019 | Rome, Italy

Neurology and Healthcare

WorldDrug Delivery and Formulations Summit

Clinical and Experimental Neuropsychology

International Conference on

Lalit Garg

University of Malta, Malta

Application of machine learning and signal processing techniques to real time detection and prediction

of epileptic seizures

pilepsy is a neurological disease, which affects around 50 million people of the world’s population and 25%

of them have medically resistant form of epilepsy. With the increased development of effective prevention

treatments, early diagnosis of epileptic seizures is becoming necessary because the patient can undergo treatments,

which can delay or prevent the disease progression. Several studies have been carried out in the past to explore the

feasibility of a practical real-time epilepsy seizure detector. However, still there is a need for improved methods of

data acquisition, feature extraction and feature space creation for epilepsy seizure detection. Also, there is no known

technique available for accurately predict a seizure onset well ahead. An accurate prediction even fewminutes before

the seizure onset might help prepare the patient, his/her caregiver. This talk will present the energy efficient real-time

seizure detection and prediction algorithms we developed [1-5], which can be implemented in wearable, non-invasive

EEG devices which would ensure prompt and effective management of seizures. The research focus also includes

development of accurate seizure detection and prediction algorithms to prevent or minimize harmful effects of seizure

onsets. Our methods [1-5] differ from previous studies mainly on two things; the first is providing a simple yet very

effective training set acquisition for epileptic seizure detection and prediction, and the second is testing these novel

approaches using a high number of seizure instances, precisely a total of 192 seizures from total 22 pediatric patients.

Biography

Lalit Garg is a Lecturer in Computer Information Systems at the University of Malta, Malta. He is also an honorary lecturer at the University

of Liverpool, UK. He has also worked as a researcher at the Nanyang Technological University, Singapore and at the University of Ulster,

UK. He received his first degree in electronics and communication engineering from the Barkatullah University, Bhopal, India, in 1999 and his

postgraduate in information technology from the ABV-Indian Institute of Information Technology and Management (IIITM), Gwalior, India in

2001. He received his Ph.D. degree from the University of Ulster, Coleraine, UK., in 2010. His research interests are missing data handling,

machine learning, data mining, mathematical and stochastic modelling and operational research, and their applications especially in the

healthcare domain. He has published over 80 technical papers in refereed high impact journals, conferences and books and has more than

550 citation count to his publications.

Figure1: Pre-processing of scalp EEG data into separate seizure and

seizure free EEG files

J Neurol Clin Neurosci, Volume 3