Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models

Islam, Torikul; Basak, Monisha; Islam, Redwanul; Roy, Amit Dutta

doi:10.1016/j.heliyon.2023.e22208

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…It is a chronic neurological disease that can affect individuals of all ages. Moreover, aging can affect epilepsy detection significantly which makes epilepsy detection more challenging 6 , 7 . The etiology of epilepsy is heterogeneous, with variations observed among individuals.…”

Section: Introductionmentioning

confidence: 99%

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Islam,

Basak

et al. 2024

Sci Rep

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Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

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Section: Introductionmentioning

confidence: 99%

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Islam,

Basak

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Identification of Epileptic Seizures using Recurrent Neural Networks and Time Series Transformer

et al. 2024

2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)

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Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models

Cited by 2 publications

References 26 publications

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Identification of Epileptic Seizures using Recurrent Neural Networks and Time Series Transformer

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