Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies

Shoeibi, Afshin; Ghassemi, Navid; Khodatars, Marjane; Moridian, Parisa; Alizadehsani, Roohallah; Zare, Assef; Khosravi, Abbas; Subaşı, Abdülhamit; Acharya, U. Rajendra; Górriz, Juan Manuel

doi:10.1016/j.bspc.2021.103417

Cited by 100 publications

(38 citation statements)

References 123 publications

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“…Finally, the performance of the algorithm was estimated using statistical metrics include specificity (Spec), sensitivity (Sens), accuracy (Acc), and F1-score (F1-S), and precision (Prec). The true positive (TP), true negative (TN), false negative (FN), and false positive (FP) parameters are extracted from the confusion matrix [110].…”

Section: Statistical Metricsmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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Section: Statistical Metricsmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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“…Several hybrid ANFIS classification schemes have been used in medicine including a genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO) algorithm for the diagnosis of type 2 diabetes mellitus (Patil et al, 2021), modified-salp swarm optimization (MSSO-ANFIS) algorithm for the heart disease diagnosis (Khan & Algarni, 2020), a subtractive clustering-based ANFIS (SCANFIS) hybrid system for the Alzheimer diagnosis (Kour et al, 2019). Additionally, an enhanced ANFIS (EANFIS) approach has been used for the classification of cancer genes (Mishra & Bhoi, 2021) whereas ANFIS and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO) and breeding swarm optimization (ANFIS-BS) methods are used for classification of epileptic seizures on electroencephalography (EEG) signals, (Shoeibi et al, 2021).…”

Section: Hybrid Anfis Classification Methods Used In Medicinementioning

confidence: 99%

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

et al. 2021

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The authors propose a method for breast dynamic contrast enhanced‐magnetic resonance imaging classification by combining radiomic texture analysis with a hybrid adaptive neuro‐fuzzy inference system (ANFIS)‐particle swarm optimization (PSO) classifier. The fast discrete curvelet transform is utilized as a decomposition scheme in multiple scales. The mean and entropy features extracted from the produced scheme are used as texture descriptors. Principal component analysis (PCA) involves reduction of the dimensionality of the initial feature set. The transformed feature vector is subsequently introduced to a hybrid ANFIS‐PSO classifier. The average overall classification power of the proposed hybrid ANFIS‐PSO classifier is comparatively assessed to that obtained using several classifiers (ANFIS, linear discriminant analysis, Naïve Bayes, artificial neural networks, random forest and support vector machine) by using the 70 training‐30 testing data ratio. The comparison performed highlights the superiority of the proposed methodology, thus underlying the potential of ANFIS‐PSO for the breast cancer diagnosis with a classification accuracy of 94%.

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“…AlexNet [ 5 ], a classical convolutional neural network proposed by Krizhevsky et al, reached a new achievement in the ImageNet Large Scale Visual Recognition Challenge (LSVRC) in 2012, and is therefore leading to a great interest in convolutional networks among researchers. Compared to earlier image processing methods, convolutional neural networks have been improved more effectively, and therefore be applied in a large number of different fields including image classification [ 5 , 6 ], semantic segmentation [ 7 ], object detection [ 8 ], object generation [ 9 ], human pose estimation [ 10 ], diagnosis of diseases using EEG signals [ 11 , 12 ], and also an amount of applications in medical field.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images

Chung

Lin

et al. 2022

Sensors

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In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the grading and staging of hepatitis according to whether portal areas have fibrous expansion, bridging with other portal areas, or bridging with central veins. Based on these staging criteria, it is necessary to identify portal areas and central veins when performing the Ishak Score staging. The bile ducts have variant types and are very difficult to be detected under a single magnification, hence pathologists must observe bile ducts at different magnifications to obtain sufficient information. This pathologic examinations in routine clinical practice, however, would result in the labor intensive and expensive examination process. Therefore, the automatic quantitative analysis for pathologic examinations has had an increased demand and attracted significant attention recently. A multi-scale inputs of attention convolutional network is proposed in this study to simulate pathologists’ examination procedure for observing bile ducts under different magnifications in liver biopsy. The proposed multi-scale attention network integrates cell-level information and adjacent structural feature information for bile duct segmentation. In addition, the attention mechanism of proposed model enables the network to focus the segmentation task on the input of high magnification, reducing the influence from low magnification input, but still helps to provide wider field of surrounding information. In comparison with existing models, including FCN, U-Net, SegNet, DeepLabv3 and DeepLabv3-plus, the experimental results demonstrated that the proposed model improved the segmentation performance on Masson bile duct segmentation task with 72.5% IOU and 84.1% F1-score.

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Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies

Cited by 100 publications

References 123 publications

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images

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