Comprehensive Analysis of EEG Datasets for Epileptic Seizure Prediction

Rahman, Rihat; Varnosfaderani, Shiva Maleki; Makke, Omar; Sarhan, Nabil J.; Asano, Eishi; Luat, Aimee F.; Alhawari, Mohammad

doi:10.1109/iscas51556.2021.9401766

Cited by 15 publications

(3 citation statements)

References 22 publications

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“…The subjects are two humans and five canines with channels from 15 to 24 per subject. The EEG recordings include a line noise of 60 Hz that could be fixed using a notch filter [34].…”

Section: American Epilepsy Society Datasetmentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer's, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.

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“…The subjects are two humans and five canines with channels from 15 to 24 per subject. The EEG recordings include a line noise of 60 Hz that could be fixed using a notch filter [34].…”

Section: American Epilepsy Society Datasetmentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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“…Compared with other brain imaging methods, EEG has the advantages of non-invasive, high temporal resolution and convenient detection [5] . In clinical, EEG is mainly used in epilepsy detection [6] , sleep detection [7] and so on. In addition, studies have shown that EEG Event-related potential (ERP) induced by acoustic stimulation can be used as biomarkers for hearing assessment [3] .…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram-based ling six-sound perception classification using deep learning

Zhang,

Tian,

Meng

et al. 2024

Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)

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Ling Six-Sound is a typical sound material widely used in hearing assessment for speech perception, which consists of /m/, /u/, /a/, /i/, /sh/ and /s/. At present, the Ling Six-Sound clinical hearing assessment mainly through subjective audiometry or question-and-answer, there are inaccurate and incomplete drawbacks. With the development of brain-computerinterface, objective perception based on EEG has made great progress. In order to construct an objective evaluation method for Ling Six-Sound perception, this study used oddball paradigm to collect passive EEG induced by Ling Six-Sound, analyzed the time and spatial domain features of the EEG signals, and used four kinds of deep learning models to classify the collected EEG signals. The experimental results show that the performance of the temporal convolutional network with the fusion layer (TCNet-Fusion) is better than other models, and the accuracy for six-classification is 79.78%, which is much higher than the random level. Based on the above results, it can be concluded that the objective Ling Six-Sound perception assessment is feasible, and provide reference for introducing this objective assessment method into clinical hearing assessment in the future.

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“…Epilepsy is one of the most common neurological disorders in humans, affecting approximately 60 million people worldwide who are unable to live normally [ 1 ]. Approximately 70–80% of epilepsy patients are well controlled with medication or surgery, but about 20–30% of patients are still not effectively treated [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Epilepsy Seizures Prediction Based on Nonlinear Features of EEG Signal and Gradient Boosting Decision Tree

Lin

2022

IJERPH

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Epilepsy is a common neurological disorder with sudden and recurrent seizures. Early prediction of seizures and effective intervention can significantly reduce the harm suffered by patients. In this paper, a method based on nonlinear features of EEG signal and gradient boosting decision tree (GBDT) is proposed for early prediction of epilepsy seizures. First, the EEG signals were divided into two categories: those that had seizures onset over a period of time (represented by InT) and those that did not. Second, the noise in the EEG was removed using complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising. Third, the nonlinear features of the two categories of EEG were extracted, including approximate entropy, sample entropy, permutation entropy, spectral entropy and wavelet entropy. Fourth, a GBDT classifier with random forest as the initial result was designed to distinguish the two categories of EEG. Fifth, a two-step “k of n” method was used to reduce the number of false alarms. The proposed method was evaluated on 13 patients’ EEG data from the CHB-MIT Scalp EEG Database. Based on ten-fold cross validation, the average accuracy was 91.76% when the InT was taken at 30 min, and 38 out of 39 seizures were successfully predicted. When the InT was taken for 40 min, the average accuracy was 92.50% and all 42 seizures selected were successfully predicted. The results indicate the effectiveness of the proposed method for predicting epilepsy seizures.

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Comprehensive Analysis of EEG Datasets for Epileptic Seizure Prediction

Cited by 15 publications

References 22 publications

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Electroencephalogram-based ling six-sound perception classification using deep learning

Epilepsy Seizures Prediction Based on Nonlinear Features of EEG Signal and Gradient Boosting Decision Tree

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