Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals

Hadiyoso, Sugondo; Wijayanto, Inung; Humairani, Annisa

doi:10.18280/ts.380107

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Zhang [161] Deep CNN, ImageNet STFT Acc 97.75% CHB-MIT Akbarian [162] Autoencoder NN DFT, effective brain connectivity Acc 97.91% Sen 97.65% Spec 98.06%…”

Section: Chb-mitmentioning

confidence: 99%

A review of automatic detection of epilepsy based on EEG signals

Ren,

Sun,

et al. 2023

J. Semicond.

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Epilepsy is a common neurological disorder that occurs at all ages. Epilepsy not only brings physical pain to patients, but also brings a huge burden to the lives of patients and their families. At present, epilepsy detection is still achieved through the observation of electroencephalography (EEG) by medical staff. However, this process takes a long time and consumes energy, which will create a huge workload to medical staff. Therefore, it is particularly important to realize the automatic detection of epilepsy. This paper introduces, in detail, the overall framework of EEG-based automatic epilepsy identification and the typical methods involved in each step. Aiming at the core modules, that is, signal acquisition analog front end (AFE), feature extraction and classifier selection, method summary and theoretical explanation are carried out. Finally, the future research directions in the field of automatic detection of epilepsy are prospected.

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“…Zhang [161] Deep CNN, ImageNet STFT Acc 97.75% CHB-MIT Akbarian [162] Autoencoder NN DFT, effective brain connectivity Acc 97.91% Sen 97.65% Spec 98.06%…”

Section: Chb-mitmentioning

confidence: 99%

A review of automatic detection of epilepsy based on EEG signals

Ren,

Sun,

et al. 2023

J. Semicond.

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“…They achieved a classification accuracy of 90.16 percent overall [8]. In addition to addictions, in some conditions such as sleep disorders and seizure, EEG signals are used and applications are developed to ensure their detection [9,10].…”

Section: Related Workmentioning

confidence: 99%

Deep and Statistical Features Classification Model for Electroencephalography Signals

Karaduman¹,

Karcı²

2022

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People strive to make sense of the complex electroencephalography (EEG) data generated by the brain. This study uses a prepared dataset to examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals from each electrode are connected to one another and are first represented as a single signal. The signal is then denoised through variation mode decomposition (VMD) during the preprocessing stage. The statistical and deep feature extraction phases are the two subsequent phases. The crucial step in the suggested strategy is to classify data using a combination of these two unique qualities. Deep and statistical feature performance was evaluated independently. Then, using the eigenvectors created by merging all of the collected features, classification was carried out using our DSFC (Deep - Statistical Features Classification) model. Although the classification accuracy rate using only statistical features was 81.2 percent and the classification accuracy rate using only deep learning was 95.71 percent, the classification accuracy rate utilizing hybrid features created using the suggested DSFC technique was 99.2%. Therefore, it can be proven that combining statistical and deep features can produce beneficial results.

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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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Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals

Cited by 10 publications

References 26 publications

A review of automatic detection of epilepsy based on EEG signals

A review of automatic detection of epilepsy based on EEG signals

Deep and Statistical Features Classification Model for Electroencephalography Signals

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

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