Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Abdulla, Shahab; Diykh, Mohammed; Alkhafaji, Sarmad K. D.; Greena, Jonathan H.; Al‐Hadeethi, Hanan; Oudah, Atheer Y.; Marhoon, Haydar Abdulameer

doi:10.3390/diagnostics12010074

Cited by 13 publications

(3 citation statements)

References 69 publications

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“…proposed Kraskov entropy and instantaneous area as features to classify interictal signals and ictal signals using the LS-SVM classifier. Al-Hadeethi et al [ 38 ] recommended the method that the multiple time-domain features combined with Kolmogorov Smirnov Test (KST) are used for feature selection and AdaBoost is used for classification. Although these studies on the classification of interictal and ictal signals have yielded encouraging results, their classification accuracy is lower than the model that we proposed.…”

Section: Discussionmentioning

confidence: 99%

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

Chen

Wang

Ren

et al. 2023

BMC Med Inform Decis Mak

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Background Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. Methods In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. Results The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. Conclusion The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

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Section: Discussionmentioning

confidence: 99%

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

Chen

Wang

Ren

et al. 2023

BMC Med Inform Decis Mak

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“…Tuncer et al (2021) proposed an automated EEG signal classification using a chaotic local binary pattern. Abdulla et al (2022) proposed a determinant of the covariance matrix model to reduce the EEG matrix dimensionality. A set of statistical features were retrieved from each period to create a feature vector.…”

Section: Software Developmentsmentioning

confidence: 99%

“…Their method showed the feasibility of detecting epileptic activity using fewer EEG channels Tuncer et al (2021). proposed an automated EEG signal classification using a chaotic local binary pattern Abdulla et al (2022). proposed a determinant of the covariance matrix model to reduce the EEG matrix dimensionality.…”

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confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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The role of artificial neural network and machine learning in utilizing spatial information

Goel

Kumar

2022

Spat. Inf. Res.

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Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Cited by 13 publications

References 69 publications

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

The role of artificial neural network and machine learning in utilizing spatial information

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