An Efficient Comparison on Machine Learning and Deep Neural Networks in Epileptic Seizure Prediction
- Title
- An Efficient Comparison on Machine Learning and Deep Neural Networks in Epileptic Seizure Prediction
- Creator
- Roseline Mary R.; Zoraida B.S.E.; Ramamurthy B.
- Description
- Electroencephalography signals have been widely used in cognitive neuroscience to identify the brains activity and behavior. These signals retrieved from the brain are most commonly used in detecting neurological disorders. Epilepsy is a neurological impairment in which the brains activity becomes abnormal, causing seizures or unusual behavior. Methods: The benchmark BONN dataset is used to compare and assess the models. The investigations were conducted using the traditional algorithms in machine learning algorithms such as KNN, naive Bayes, decision tree, random forest, and deep neural networks to exhibit the DNN models efficiency in epileptic seizure detection. Findings: Experiments and results prove that deep neural network model performs more than traditional machine learning algorithms, especially with the accuracy value of 97% and area under curve value of 0.994. Novelty: This research aims to focus on the efficiency of deep neural network techniques compared with traditional machine learning algorithms to make intelligent decisions by the clinicians to predict if the patient is affected by epileptic seizures or not. So, the focus of this paper helps the research community dive into the opportunities of innovations in deep neural networks. This research work compares the machine learning and deep neural network model, which supports the clinical practitioners in diagnosis and early treatment in epileptic seizure patients. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 677-687.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Area under curve (AUC); BONN dataset; Deep neural networks (DNN); Electroencephalogram (EEG); Epilepsy
- Coverage
- Roseline Mary R., Bharathidasan University, Tamil N?du, Tiruchirappalli, India, CHRIST (Deemed To Be University), Karnataka, Bangalore, India; Zoraida B.S.E., Bharathidasan University, Tamil N?du, Tiruchirappalli, India; Ramamurthy B., CHRIST (Deemed To Be University), Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23674512
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Roseline Mary R.; Zoraida B.S.E.; Ramamurthy B., “An Efficient Comparison on Machine Learning and Deep Neural Networks in Epileptic Seizure Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18671.