Epilepsy Detection Using Supervised Learning Algorithms

Title

Epilepsy Detection Using Supervised Learning Algorithms

Creator

Krishnasamy L.; Sriwastav Y.K.; Bharat S.P.; Ganachar S.R.

Description

In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE.

Source

Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Convolution neural network; Electroencephalogram (EEG); Epilepsy; K-Nearest Neighbour; Random Forest; Seizures; Supervised learning algorithms; Support vector machine

Coverage

Krishnasamy L., Nandha Engineering College, Department of Artificial Intelligence and Data Science, Erode, India; Sriwastav Y.K., School of Engineering and Technology, Christ University, Department of CSE, Bengaluru, India; Bharat S.P., School of Engineering and Technology, Christ University, Department of CSE AIML, Bengaluru, India; Ganachar S.R., School of Engineering and Technology, Christ University, Department of CSE, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835038365-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC460750.2024.10649398

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203816395&doi=10.1109%2fInC460750.2024.10649398&partnerID=40&md5=545acdc4a94e2345d940aff13793d0cd