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Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform [article]

Mohammad Reza Yousefi, Saina Golnejad, Melika Mohammad Hosseini
2022 arXiv   pre-print
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

Sani Saminu, Guizhi Xu, Zhang Shuai, Isselmou Abd El Kader, Adamu Halilu Jabire, Yusuf Kola Ahmed, Ibrahim Abdullahi Karaye, Isah Salim Ahmad
2022 Applied Sciences  
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

Sreelekha Panda, Raajdhani Engineering College, Bhubaneswar, India
2019 International Journal of Advanced Trends in Computer Science and Engineering  
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

Mohit Kumar, Ram Pachori, U. Acharya
2017 Entropy  
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

Deng-ao Li, Jie Zhou, Jumin Zhao, Xinyan Liu
2018 Mathematical Problems in Engineering  
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

Milind Natu, Mrinal Bachute, Shilpa Gite, Ketan Kotecha, Ankit Vidyarthi, Deepika Koundal
2022 Computational and Mathematical Methods in Medicine  
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

Prasanna J., M. S. P. Subathra, Mazin Abed Mohammed, Mashael S. Maashi, Begonya Garcia-Zapirain, N. J. Sairamya, S. Thomas George
2020 Sensors  
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

Abhijit Bhattacharyya, Ram Pachori, Abhay Upadhyay, U. Acharya
2017 Applied Sciences  
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

Ijaz Ahmad, Xin Wang, Mingxing Zhu, Cheng Wang, Yao Pi, Javed Ali Khan, Siyab Khan, Oluwarotimi Williams Samuel, Shixiong Chen, Guanglin Li, Muhammad Zubair Asghar
2022 Computational Intelligence and Neuroscience  
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  
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

Ruchi Sharma, Khyati Chopra
2021 Zenodo  
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

He Sheng Liu, Tong Zhang, Fu Sheng Yang
2002 IEEE Transactions on Biomedical Engineering  
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

Protima Khan, Md. Fazlul Kader, S. M. Riazul Islam, Aisha B. Rahman, Md. Shahriar Kamal, Masbah Uddin Toha, Kyung-Sup Kwak
2021 IEEE Access  
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

Yissel Rodriguez Aldana, Borbala Hunyadi, Enrique Juan Maranon Reyes, Valia Rodriguez Rodriguez, Sabine Van Huffel
2018 IEEE journal of biomedical and health informatics  
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

Nibras Abo Alzahab, Luca Apollonio, Angelo Di Iorio, Muaaz Alshalak, Sabrina Iarlori, Francesco Ferracuti, Andrea Monteriù, Camillo Porcaro
2021 Brain Sciences  
, 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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