Dissimilarity-based time–frequency distributions as features for epileptic EEG signal classification

Ech-Choudany, Youssef; Scida, Daniel; Assarar, Mustapha; Landré, Jérôme; Bellach, Benaissa; Morain-Nicolier, Frédéric

doi:10.1016/j.bspc.2020.102268

Cited by 16 publications

(6 citation statements)

References 35 publications

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“…In this task, EEG signals were recorded occasionally from healthy participants and patients with epileptic symptoms [ 20 ]. Seven ML and DL algorithms were used in SD studies: CNN [ 265 , 266 ], SVM [ 116 , 118 , 179 , 180 , 182 , 184 , 191 , 192 , 198 , 200 , 207 , 267 ], KNN [ 189 , 268 , 269 ], ANNs [ 183 , 199 ], RF [ 185 , 187 , 190 ], LDA [ 186 ], and ELM [ 181 ]. However, among the 24 studies focused on seizures, 12 applied the SVM algorithm with various kernels.…”

Section: Discussionmentioning

confidence: 99%

“… 24 channels Own database TQWT MC-LSVM Accuracy = 95.7 [ 175 ] 2019 MI 4 subj. 59 channels BCI Competition 2008 CSP BPNN SVM Accuracy = 91.6 [ 186 ] 2021 SD BONN database Dissimilarity-based TFD LDA ANNs SVM Accuracy = 98 [ 240 ] 2018 MWL OneR database FFT/Entropy RF SVM Accuracy = 87.2 [ 65 ] 2011 ER 20 subj. 62 channels Own database DWT KNN Accuracy = 82.87 [ 241 ] 2017 MWL Own database FFT KNN SVM ANNs Accuracy = 90.5 [ 173 ] 2014 …”

Section: Table A1mentioning

confidence: 99%

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Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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Section: Discussionmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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“…The windowing process is important in its pre-processing module because it deals with the stationary principle as it is a Time-frequency method. Reference [31] compared Time-Frequency Distributions (TFD) and many dissimilarity measures for epileptic seizures detection. Their framework was evaluated on 13 different classification problems and was found effective in the detection of seizures (Fig.…”

Section: B Music Methodmentioning

confidence: 99%

A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Sachadev

Bhatnagar

2021

Studies in Computational Intelligence

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Medical images play an important role in the diagnosis of diseases effectively. Human brain is consisting of millions of neurons which work in proper coordination with one another, and human behavior is an outcome of the response of neurons to internal/external motor or sensory stimuli. These neurons are the carriers of signals from different parts of the human body and the brain. Human cognition studies focus on interpreting either these signals or brain images and there are various technologies which are in use for brain disease studies. Every image or technology generates lot of data in different form which can be modeled using Artificial Intelligence models. The electroencephalogram (EEG) is a recording of the electrical activity of the brain from the scalp. The recorded waveforms reflect the cortical electrical activity. Signal intensity: EEG activity is quite small, measured in microvolts (mV). Delta, Theta, Alpha and Beta are the main frequencies of human EEG waves and EEG electrodes are used to generate EEG plots. In this chapter, authors have tried to capture details on various technologies, techniques, usage of AI models in Brain mapping for disease predictions. This chapter presents a comprehensive account on them and will appeal to researchers in the field of human cognition.

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“…Researchers use different pre-processing techniques for accuracy enhancement. In literature different mathematical models are used as pre-processing techniques like empirical mode decomposition (EMD) [2], ensemble EMD [3], dissimilarity based frequency distribution [4], based on high order statistical parameter tuning [5][6], feature extraction based on discrete wavelet transform (DWT) [7], composite multiscale dispersion entropy [8], etc. For the ever-growing demand for correct diagnosis, various algorithms are developed [9].…”

Section: Introductionmentioning

confidence: 99%

Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

Dutta

Manohar

Indira

et al. 2022

Preprint

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Background Based on the symptoms experienced during the episode and the Electroencephalograph (EEG) recording made during the inter-ictal phase, the doctor makes the epileptic seizure type diagnosis. The fundamental issue, however, is that patients frequently struggle to explain their symptoms in the absence of an observer and identify traces in inter-ictal EEG patterns. Aims This study examines electroencephalographic (EEG) signals from epileptic seizures in order to diagnose seizures in pre-ictal, ictal, and inter-ictal stages and to categorize them into seven groups. Methods For the investigation, a licensed dataset from Temple University Hospital was used. Seven different seizure types are pre-processed from the seizure corpus and divided into pre-ictal, ictal, and inter-ictal stages. K-Nearest Neighbor (KNN), Random Forest, and other machine and deep learning techniques were used to perform the multi-class categorization. Result With 20 channels and an 80 − 20 train-test ratio, multiclass classification of seven different types of epileptic seizures was accomplished. For the pre-ictal, ictal, and inter-ictal stages, weighted KNN achieved accuracy levels of 94.7%, 94.7%, 69.0% during training and 94.46%, 94.46%, and 71.11% during testing. Conclusion Seven epileptic seizure type classification using machine learning techniques carried out with MATLAB software and weighted KNN shows better accuracy comparatively.

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Dissimilarity-based time–frequency distributions as features for epileptic EEG signal classification

Cited by 16 publications

References 35 publications

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

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