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Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
[article]
2022
arXiv
pre-print
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Also, the value of using electroencephalogram signal has been evaluated in two ways: using wavelet transform and non-using wavelet transform. ...
In the second step, the wavelet transform technique was used to process data. ...
the flexible analytic wavelet transform (FAWT) for obtaining the decomposition coefficients of EEG signals.This nonstationary transform produced fractal dimension features at each of the scaling levels.This ...
arXiv:2204.04488v1
fatcat:vkdleoqqw5d7vbguvlirhyewfu
Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals
2022
Applied Sciences
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The detection of the location of focal EEG signals and the time of seizure occurrence are vital information that help doctors treat focal epileptic seizures using a surgical method. ...
Four detection and classification techniques for focal and non-focal EEG signals were proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). ...
Focal and non-focal EEG signals were decomposed into sub-bands using Flexible Analytic Wavelet Transform (FAWT), while the features were then extracted using a fractal dimension in Dalal et al. ...
doi:10.3390/app12104879
fatcat:d752224fcbde3l3d3lr6qrhuby
Detection and Classification Methods for EEG Epileptic Seizures
2019
International Journal of Advanced Trends in Computer Science and Engineering
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This paper provides an overview of detection and classification era for the reason of EEG seizure. ...
Time frequency transforms and system learning plays an important function in extracting meaningful facts. ...
The efficiency of seizure detection and relegation depends on features extracted from different signal processing transforms. ...
doi:10.30534/ijatcse/2019/40862019
fatcat:hp6rng5rvzcrdfodvud5okgeo4
Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework
2017
Entropy
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Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. ...
We have analyzed normal and ECG beats using sample entropy (SEnt) in flexible analytic wavelet transform (FAWT) [20, 21] framework. ...
Mathematical expressions for filters and perfect reconstruction condition of flexible analytic wavelet transform (FAWT). ...
doi:10.3390/e19090488
fatcat:4c2o5puojffufkql77fveftrau
J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals
2018
Mathematical Problems in Engineering
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Therefore, a new technique which is capable of detecting J wave using analytic time-frequency flexible wavelet transformation (ATFFWT) is proposed in this paper. ...
We have used ATFFWT to decompose the processed ECG signals into the desired subbands. Further, Fuzzy Entropy (FE) is computed from each subband to capture more hidden and meaningful information. ...
Then, to cope with the nonlinear and nonstationary nature of ECG signals, analytic time-frequency flexible wavelet transformation (ATFFWT) is employed to decompose the signals in terms of subbands signals ...
doi:10.1155/2018/6791405
fatcat:x5bo6yrlkrcebfxw6i2oixe6b4
Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches
2022
Computational and Mathematical Methods in Medicine
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These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. ...
Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. ...
Research Gaps of Existing Feature extraction and feature reduction: wavelet transformations are commonly used in the biomedical field for feature extraction. ...
doi:10.1155/2022/7751263
pmid:35096136
pmcid:PMC8794701
fatcat:mzlwvlfs35djtcqrfaomcqj2fi
Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
2020
Sensors
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Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN ...
The extracted features detail the nonlinearity in the NFC and the FC EEG signals. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20174952
pmid:32883006
fatcat:l4iszfv3tnhvpkjdg4volvaoim
Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals
2017
Applied Sciences
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of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier ...
The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app7040385
fatcat:cgx2qzeaufhknovwxmn3ihlbqy
EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
2022
Computational Intelligence and Neuroscience
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Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection. ...
Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might ...
Rajendra Acharya, "A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension," Pattern Recognition Letters, vol. 94, pp. 172-179, ...
doi:10.1155/2022/6486570
pmid:35755757
pmcid:PMC9232335
fatcat:kfjnjra4hfakfosmpe6kexweae
Enhanced firefly optimizer with deep neural network for the detection of epileptic seizures using EEG signals
2020
International Journal of Engineering and Advanced Technology
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Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. ...
Then, Chebyshev type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. ...
In this literature, the original EEG signals were acquired from BB EEG database. Then, empirical wavelet transform was utilized for decomposing the signals into time frequency bands. ...
doi:10.35940/ijeat.d6741.069520
fatcat:w4yww3t7b5e7tluzmh47ei7jpu
Enhanced firefly optimizer with deep neural network for the detection of epileptic seizures using EEG signals
2021
Zenodo
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Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. ...
Then, Chebyshev type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. ...
In this literature, the original EEG signals were acquired from BB EEG database. Then, empirical wavelet transform was utilized for decomposing the signals into time frequency bands. ...
doi:10.5281/zenodo.5545660
fatcat:s6kg3hjcvfemnjpma7xohylmre
A multistage, multimethod approach for automatic detection and classification of epileptiform EEG
2002
IEEE Transactions on Biomedical Engineering
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Index Terms-Adaptive filtering, artificial neural network, electroencephalogram (EEG), epilepsy, wavelet transform. ...
The present study proposes a robust system that combines multiple signal-processing methods in a multistage scheme, integrating adaptive filtering, wavelet transform, artificial neural network, and expert ...
Feature Extraction If the wavelet coefficients of the predefined scales are to be analyzed further, some feature parameters of the wavelet coefficients must be extracted first. ...
doi:10.1109/tbme.2002.805477
pmid:12549737
fatcat:pu2g4znrvfb33h2vea7cpfkine
Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances
2021
IEEE Access
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Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. ...
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. ...
Classification of the EEG signals into focal and non-focal signals using soft computing methods was performed in [132] . ...
doi:10.1109/access.2021.3062484
fatcat:lmhp34ad3zdexb5y4bt5ksntia
Nonconvulsive Epileptic Seizure Detection in Scalp EEG Using Multiway Data Analysis
2018
IEEE journal of biomedical and health informatics
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To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. ...
The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition (BTD) of the EEG data represented as third order tensor. ...
The database used in these publications consists of 13 EEG records, 6 of which have only one seizure. The parameters extracted from epochs of the same seizure tend to display a high similarity. ...
doi:10.1109/jbhi.2018.2829877
pmid:29994034
fatcat:i4axs44p75ch5dxrwpgnzk2h2a
Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review
2021
Brain Sciences
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, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used ...
Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the ...
The authors acknowledge all of the anonymous reviewers for their constructive comments that helped to improve the quality of this review paper. ...
doi:10.3390/brainsci11010075
pmid:33429938
fatcat:mh6naeofzjahjbi4bikub65i2i
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