AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

Jahan, Sobhana; Nowsheen, Farhana; Antik, Mahathir Mahmud; Rahman, Md. Sazzadur; Kaiser, M. Shamim; Hosen, A. S. M. Sanwar

doi:10.1109/access.2023.3251105

Cited by 18 publications

(2 citation statements)

References 162 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the authors examined the significance of implementing machine learning models for the development of seizure detection system. Jahan et al 17 conducted a systematic review to analyze several AI mechanisms for the prediction of epileptic seizure. The authors intends to study various EEG approaches' operating principles and specific applications.…”

Section: Related Workmentioning

confidence: 99%

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Golla,

Maloji

2023

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time‐consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non‐linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time‐frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE‐GTRN‐LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE‐based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure‐affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE‐GTRN‐LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state‐of‐the‐art techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Golla,

Maloji

2023

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…Epilepsy is a chronic brain disease with recurrent seizures and a prevalence of 1% [1]. It is one of the most serious chronic dysfunction syndromes caused by the abnormally synchronous discharge of neurons [2]. Epileptic seizures have the characteristics of being recurrent and sudden.…”

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

View full text Add to dashboard Cite

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.

show abstract

A Smart IoT-Cloud Framework with Adaptive Deep Learning for Real-Time Epileptic Seizure Detection

Hussein,

Alomari,

Almomani

et al. 2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

Cited by 18 publications

References 162 publications

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

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

A Smart IoT-Cloud Framework with Adaptive Deep Learning for Real-Time Epileptic Seizure Detection

Contact Info

Product

Resources

About