Epileptic Seizure Detection Using Deep Bidirectional Long Short-Term Memory Network

Thakur, Mahima; Snekhalatha, U.; Shafi, Mohammed; Gupta, Saumya; Roy, Sourabh Ranjan; Vineetha, S.

doi:10.1007/978-981-16-5157-1_69

Cited by 2 publications

(1 citation statement)

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“…Machine learning (ML) and deep learning (DL) algorithms have gained a reputation in the past few years for seizure classification [12][13][14][15]. One of the main challenges of machine learning (ML) and deep learning (DL) algorithms is the availability of long-term EEG data.…”

Section: Introductionmentioning

confidence: 99%

General and patient-specific seizure classification using deep neural networks

Massoud

Abdelzaher

Kuhlmann

et al. 2023

Analog Integr Circ Sig Process

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Seizure prediction algorithms have been central in the field of data analysis for the improvement of epileptic patients’ lives. The most recent advancements of which include the use of deep neural networks to present an optimized, accurate seizure prediction system. This work puts forth deep learning methods to automate the process of epileptic seizure detection with electroencephalogram (EEG) signals as input; both a patient-specific and general approach are followed. EEG signals are time structure series motivating the use of sequence algorithms such as temporal convolutional neural networks (TCNNs), and long short-term memory networks. We then compare this methodology to other prior pre-implemented structures, including our previous work for seizure prediction using machine learning approaches support vector machine and random under-sampling boost. Moreover, patient-specific and general seizure prediction approaches are used to evaluate the performance of the best algorithms. Area under curve (AUC) is used to select the best performing algorithm to account for the imbalanced dataset. The presented TCNN model showed the best patient-specific results than that of the general approach with, AUC of 0.73, while ML model had the best results for general classification with AUC of 0.75.

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Section: Introductionmentioning

confidence: 99%