A novel epilepsy seizure prediction model using deep learning and classification Article Swipe
B. Jaishankar
,
Anlin Sahaya Infant Tinu M
,
D. Vidyabharathi
,
Laxmi Raja
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.health.2023.100222
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.health.2023.100222
Epilepsy is a common neurological disease where the earlier disease prediction significantly impacts those patients’ lives. In this paper, a novel epilepsy seizure prediction approach is designed using deep learning. The proposed model is applied to the Electroencephalogram (EEG) recordings collected from Children’s Hospital Boston (CHB-MIT). The recording data is grouped into 23 cases, including 17 females and five males of different ages. The recordings are sampled at 256 samples/s of 16-bit resolution. The target is to analyse the brain’s state and evaluate the changes encountered from the interictal state. The earlier prediction process helped in timely disease identification and treatment to rescue the patients. Feeding the raw EEG signals over the feature extractor reduces the computational complexity and execution time. An Adaptive Grey Wolf Optimizer (AGWO) is used for learning the features and promoting those discriminative features to enhance the prediction rate and classification accuracy. To optimize the features integrating the auto-encoder concept with Genetic Algorithm (GA) in an adaptive manner termed as to enhance the prediction rate. The functionality of is tested over the subjects of the CHB-MIT EEG dataset to achieve resourceful outcomes. The proposed attains higher accuracy of 99% and reduces the False Alarm Rate (FAR) with little prediction time. The model’s functionality is evaluated using the MATLAB simulation environment and shows a better trade-off than existing approaches.
Related Topics
Concepts
Computer science
Artificial intelligence
Ictal
Epilepsy
Electroencephalography
Discriminative model
Constant false alarm rate
Feature (linguistics)
Deep learning
Machine learning
Autoencoder
Extractor
Pattern recognition (psychology)
Epileptic seizure
MATLAB
Medicine
Philosophy
Psychiatry
Engineering
Operating system
Linguistics
Process engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.health.2023.100222
- OA Status
- gold
- Cited By
- 23
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383879161
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4383879161Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.health.2023.100222Digital Object Identifier
- Title
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A novel epilepsy seizure prediction model using deep learning and classificationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-11Full publication date if available
- Authors
-
B. Jaishankar, Anlin Sahaya Infant Tinu M, D. Vidyabharathi, Laxmi RajaList of authors in order
- Landing page
-
https://doi.org/10.1016/j.health.2023.100222Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.health.2023.100222Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Ictal, Epilepsy, Electroencephalography, Discriminative model, Constant false alarm rate, Feature (linguistics), Deep learning, Machine learning, Autoencoder, Extractor, Pattern recognition (psychology), Epileptic seizure, MATLAB, Medicine, Philosophy, Psychiatry, Engineering, Operating system, Linguistics, Process engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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23Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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