Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree

Albaqami, Hezam; Hassan, Ghulam Mubashar; Subaşı, Abdülhamit; Datta, Amitava

doi:10.1016/j.bspc.2021.102957

Cited by 63 publications

(48 citation statements)

References 46 publications

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“…The present work emphasizes performing classification without involving any complex feature extraction, and the potential capacity of DL algorithms has provided a new roadmap to reduce the complexity of feature extraction. Different deep learning algorithms, such as decision tree [ 32 ], SVM [ 33 ], random forest [ 34 , 35 ], and recurrent neural networks (RNN) [ 36 ], based approaches are used widely for epileptic detection. Feature extraction is essential to perform before classification since it can directly process EEG samples before feeding them into the classifier.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Dhar

Garg

Rahman

2022

Journal of Healthcare Engineering

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One of the most common neurological disorders is epilepsy, which disturbs the nerve cell activity in the brain, causing seizures. Electroencephalography (EEG) signals are used to detect epilepsy and are considered standard techniques to diagnose epilepsy conditions. EEG monitors and records the brain activity of epilepsy patients, and these recordings are used in the diagnosis of epilepsy. However, extracting the information from the EEG recordings manually for detecting epileptic seizures is a difficult cumbersome, error-prone, and labor-intensive task. These negative attributes of the manual process increase the demand for implementing an automated model for the seizure detection process, which can classify seizure and nonseizures from EEG signals to help in the timely identification of epilepsy. Recently, deep learning (DL) and machine learning (ML) techniques have been used in the automatic detection of epileptic seizures because of their superior classification abilities. ML and DL algorithms can accurately classify different seizure conditions from large-scale EEG data and provide appropriate results for neurologists. This work presents a feature extraction-based convolutional neural network (CNN) to sense and classify different types of epileptic seizures from EEG signals. Different features are analyzed to classify seizures via EEG signals. Simulation analysis was managed to investigate the classification performance of the hybrid CNN-RNN model in terms of different achievement metrics such as accuracy, precision, recall, f1 score, and false-positive rate. The results validate the efficacy of the CNN-RNN model for seizure detection.

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Section: Related Workmentioning

confidence: 99%

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Dhar

Garg

Rahman

2022

Journal of Healthcare Engineering

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“…Sharma combined wavelet subspace features and support vector machine for EEG driven epilepsy diagnosis (Sharma et al, 2020 ). Albaqami studied the automatic EEG signal classification using WPT and gradient boosting decision tree (Albaqami et al, 2021 ). Movahed employed a special orthogonal wavelet filter-bank for EEG decomposition and combined it with machine learning for diagnose the disease of major depressive disorder (Movahed et al, 2021 ).…”

Section: Joint Of Wt and Artificial Intelligence For Intelligent Analysis Of Eegmentioning

confidence: 99%

Potential of Overcomplete Wavelet Frame Expansion for Facilitating Electroencephalogram Information Mining

et al. 2022

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“…Among timefrequency methods, Wavelet Transforms (WT) based feature extraction is the most promising method to extract robust features from EEG signals [16]. The strategies in wavelet-based feature extraction from EEGs use Continues Wavelet Transform (CWT) [17], Discrete Wavelet Transform (DWT) [18], Wavelet Packet Decomposition (WPD) [19,18], Tunable-Q Factor Wavelet Transform (TQWT) [20] and Dual tree wavelet transform (DTCWT) [21].…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Wavelet Transform (WT) methods have been employed successfully to solve various non-stationary signal problems [16,33,34], including EEG [19]. WT is a spectral estimation method that provides another representation of the signal The output of each level are two down sampled components: Approximation Aj and Detail Dj which are represented as:…”

Section: Feature Extractionmentioning

confidence: 99%

Wavelet Based Multi-Class Seizure Type Classification System

Albaqami

Hassan²,

Datta

2022

SSRN Journal

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Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree

Cited by 63 publications

References 46 publications

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals

Potential of Overcomplete Wavelet Frame Expansion for Facilitating Electroencephalogram Information Mining

Wavelet Based Multi-Class Seizure Type Classification System

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