Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
- Title
- Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
- Creator
- Najmusseher; Banu P.K.N.; Janardhan D.C.
- Description
- In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification. Copyright 2024 Inderscience Enterprises Ltd.
- Source
- International Journal of Intelligent Engineering Informatics, Vol-12, No. 4, pp. 460-512.
- Date
- 2024-01-01
- Publisher
- Inderscience Publishers
- Subject
- artificial intelligence; BCI; brain-computer interaction; EEG signals; epileptic seizure onset classification; extreme gradient boosting; fast Fourier transformation; IDBN; improved deep belief network
- Coverage
- Najmusseher, Department of Computer Science, CHRIST (Deemed to Be University), Central Campus, Bangalore, 560029, India; Banu P.K.N., Department of Computer Science, CHRIST (Deemed to Be University), Central Campus, Bangalore, 560029, India; Janardhan D.C., Bangalore Medical College and Research Institute, Government of Karnataka Bangalore Medical College and Research Institute, Government of Karnataka, Bangalore, 560002, India
- Rights
- Restricted Access
- Relation
- ISSN: 17588715
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Najmusseher; Banu P.K.N.; Janardhan D.C., “Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network,” CHRIST (Deemed To Be University) Institutional Repository, accessed November 4, 2025, https://archives.christuniversity.in/items/show/13420.