Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Kader, Isselmou Abd El; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim

doi:10.11113/elektrika.v19n2.219

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…As a result, to avoid complexity increase with little to no performance advantage, only crucial features should be added. The wavelet transform has also been utilized with entropy and statistical characteristics [137].…”

Section: Discussionmentioning

confidence: 99%

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

jain

2023

Preprint

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BrainWave Diagnostics, an emerging field, leverages electroencephalography (EEG) data for cost-effective and resource-efficient neurological disorder detection. Although EEGs are commonly used for neurological disease detection, their low signal intensity and nonlinear features pose analytical challenges. This review explores the use of high-performance computational tools, machine learning, and deep learning methods in diagnosing a range of neurological disorders, including epilepsy, Parkinson's disease, autism, ADHD, stroke, tumors, schizophrenia, Alzheimer's, depression, and alcohol use disorder. The increasing prevalence of neurological disorders and their resource burden underscores the urgency of these diagnostic advancements. Future research can consider multi-modal approaches, providing practical solutions for neurological disorder detection beyond EEGs, with potential applications in diverse signal analysis domains.

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Section: Discussionmentioning

confidence: 99%

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

jain

2023

Preprint

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“…Hence, only significant features should be included to avoid increasing complexity with little or no improvement in performance. Wavelet transform combined with other techniques such as entropy and statistical parameters has also been employed [64]. Figure 7 shows the percentage of conventional methods used by researchers based on our reviewed articles' analysis, while Figure 8 depicts the comparison of conventional techniques and deep learning models in percentages employed by researchers from 2014 to 2020.…”

Section: Discussionmentioning

confidence: 99%

“…These features describe the EEG static behavior in time and space as well as dynamic properties. Feature extraction techniques commonly found in the literature include time domain, frequency domain and time-frequency analyses, wavelet analysis, energy distribution, entropy analysis and feature tensors [64]. However, recently, most CAD systems use two or more methods combined as a hybrid technique.…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Saminu

Zhang

et al. 2021

Brain Sciences

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The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

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“…EEG represents a chaotic detection method because it produces blurred images as well as fails to represent the state and sites of the tissues of the brain [ 5 ]. The medical diagnosis of epilepsy is usually performed manually using EEG signals, which is very complex and requires highly skilled professional neurologists [ 6 ]. EEG signals are categorized into two types: (i) scalp EEG and (ii) iEEG.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

et al. 2023

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The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

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Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

Cited by 10 publications

References 44 publications

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

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