Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods

Srinath, Rajagopalan; Gayathri, R.

doi:10.1002/ima.22486

Cited by 20 publications

(15 citation statements)

References 26 publications

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“…The highest accuracy result achieved was 94% for KNN, while the DT and SVM obtained the same accuracy of 88%. Whereas [21], used EEG signal to classify two epileptic seizures (focal and non-focal) by using four types of feature techniques using probability density function (PDF), energy and pattern spectrum entropy (PSE), mutual information (MI), and characteristic feature vector (CFR). Further, the outcome of feature techniques used as input for the adaptive neurofuzzy inference system classification technique, which is a kind of artificial neural network (ANN).…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of Epileptic Seizure Signatures Classification in EEG Using Supervised Machine Learning Algorithms

Al-jumaili¹,

Duru²,

İbrahim³

et al. 2023

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Epilepsy is one of the earnest neurological disorders that require further social attention. Based on the International League Against Epilepsy (ILAE), which classifies the epilepsy term as a number of several seizures that occur in the brain. Electroencephalography (EEG) is considered our brain window to the electrical activity. It is a significant device used for diagnosing multiple brain disorders such as Epilepsy. Moreover, this study used data from Temple University Hospital Seizure Corpus (TUH), which represents an accurate description of the clinical cases for five types of epileptic seizures. Initially, to extract information from EEG signals, three types of feature extraction have been used namely Fast Fourier Transform, Entropy, and Approximate Entropy. Due to the high degree of variance of EEG signals, we implemented a band-pass filter to divide the signals into sub-bands called delta rhythm (0.1 -4Hz), theta rhythm (5 -9Hz), alpha rhythm (10 -14Hz), beta rhythm (15-31Hz), and gamma rhythm (32-100). The feature extraction outcome underwent normalization techniques and was used as input for the classifiers. Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) classifier have implemented in order to classify (1) second epoch length window. In the first scenario, we applied the FFT features to the classifiers, the results showed that SVM obtained the highest value compared to the other classifiers with 96% accuracy, whereas KNN was 92% and the DT and NB were 76% and 67%, respectively. The second scenario was applying entropy features to the classifiers, the results of classification were 91% for SVM and 88% for KNN, while the DT and NB were 76% and 67%, respectively. The last scenario was ApEn, which shows that SVM still gains the highest value, which was 83%, and 76% for KNN, where the DT and NB were 65% and 69%, respectively. From the aforementioned results, we deduced that SVM achieved the best accuracy when applied with the three feature extractions.

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Section: Literature Reviewmentioning

confidence: 99%

Investigation of Epileptic Seizure Signatures Classification in EEG Using Supervised Machine Learning Algorithms

Al-jumaili¹,

Duru²,

İbrahim³

et al. 2023

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“…As a result, ANN, FIS, and ANFIS output 92.3, 88, and 96%, respectively. A paper by (Srinath & Gayathri, 2021) carried out classification using soft computing methods along with ANFIS algorithm which results in an accuracy of 99.4%.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…The model improved the robustness of ANFIS technique and amplified the classification accuracy results. Furthermore, [91] implemented ANFIS for data classification with the accuracy of 99.4%, specificity, and sensitivity of 99.7%. Table 8 shows the list of papers which utilized NF for in their proposed classification model.…”

Section: Exclusion Criteria Inclusion Criteriamentioning

confidence: 99%

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran

Keikhosrokiani

2022

Neural Comput & Applic

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The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

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“…The observations suggested that the fine Gaussian SVM was most efficient. Classification of the EEG signals into focal and non-focal signals using soft computing methods was performed in [132]. The whole process comprises three modules: transformation, feature computation, and feature classifications.…”

Section: ) Ml-based Approaches In Epilepsy Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods

Cited by 20 publications

References 26 publications

Investigation of Epileptic Seizure Signatures Classification in EEG Using Supervised Machine Learning Algorithms

Investigation of Epileptic Seizure Signatures Classification in EEG Using Supervised Machine Learning Algorithms

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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