Classification of Seizure Through SVM Based Classifier

Shahid, Abdulla; Wahab, Mohd; Farooq, Omar; Rafiuddin, Nidal

doi:10.1109/iccci.2018.8441364

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…A random forest algorithm was applied to the resting-state functional magnetic resonance imaging (MRI) [10], and a fast single shot proximal SVM was applied to functional MRI images to predict whether the epilepsy has a potential risk of seizure [11]. Five types of epilepsy were classified using SVM and structural MRI and showed a high classification performance of 91% [12]. Then, a cascade of machine learning classifiers were integrated, and bio-inspired heuristics were involved to detect the seizures [13].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data

Qu,

Ji,

Wang

et al. 2023

Bioengineering

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Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.

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