Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals

Rafiammal, S. Syed; Jamal, D. Najumnissa; Mohideen, S. Kaja

doi:10.48084/etasr.3419

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The evaluation step is finished by implementing the proposed method. For that, the CAD framework was deployed into a Raspberry Pi 4 [34] tied with the EEG device Emotiv [37]. This phase is evaluated with regard to processing time, CPU usage, and memory demand.…”

Section: Discussionmentioning

confidence: 99%

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Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals

Ayed

2020

Eng. Technol. Appl. Sci. Res.

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The revolution in technology affects many fields and among them the Healthcare system. The application-based computer was developed to help specialists to detect diseases, and to perform some basics operations. In this paper, focus is given on the proposed attempts to detect Epilepsy Disease (ED). Several Computer-Aided Diagnosis (CAD) methods were used to provide the brain’s disease status according to signals related to brain activities. These applications achieved acceptable results but still have their limitations. An intelligence CAD based on the Balanced Communication-Avoiding Support Vector Machine (BCA-SVM) is proposed to detect ED using Electroencephalogram (EEG) signals. This attempt is implemented on a Raspberry Pi 4 as a real board to ensure real-time processing. The CAD-based on BCA-SVM achieved an accuracy of 99.8% and the execution time was around 3.2s satisfying the real-time requirement.

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Section: Discussionmentioning

confidence: 99%

“…Hence, algorithms were not executed in real-time due to the considerable computation time they demanded. Authors in [34] proposed a hardware implementation based on an FPGA board to ensure real-time detection. The authors used the SVM algorithm and achieved an accuracy of 86%.…”

Section: Open Challengesmentioning

confidence: 99%

Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals

Ayed

2020

Eng. Technol. Appl. Sci. Res.

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Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Handa,

Lavanya,

Goel

et al. 2024

Artif Intell Rev

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Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.

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Detection of Epilepsy Seizure in Adults Using Discrete Wavelet Transform and Cluster Nearest Neighborhood Classifier

Rafiammal

Jamal

Mohideen

2021

Iran J Sci Technol Trans Electr Eng

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Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals

Cited by 7 publications

References 17 publications

Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals

Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Detection of Epilepsy Seizure in Adults Using Discrete Wavelet Transform and Cluster Nearest Neighborhood Classifier

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