Journal
INTERNET OF THINGS
Volume 14, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.iot.2019.03.002
Keywords
Data Mining; Epilepsy; Electroencephalogram; Support Vector Machine; Signal processing; Sliding window
Categories
Funding
- GNCS-INDAM
- Italian PRORA
Ask authors/readers for more resources
The study focuses on using Data Mining techniques to analyze EEG signals for automatic seizure detection, achieving over 99% accuracy through a trained classifier and feature extraction on publicly available datasets.
Epilepsy is a chronic neurological disorder characterized by frequent seizures, which severely impact the quality of life of epilepsy patients and sometimes are accompanied by loss of consciousness. The most widely accepted and used tool by epileptologists to identify seizures and diagnose epilepsy is the ElectroEncephaloGram (EEG). Seizure detection on EEG signals is a long process, which is done manually by epileptologists. This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals. We have designed and developed a multipurpose and extendable tool for feature extraction from time series data, named Training Builder. Our trained classifier, based on signal processing, sliding window paradigm, features extraction and selection, and Support Vector Machines, showed excellent results, reaching an accuracy of over 99% during the test made on publicly available EEG datasets. (C) 2019 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available