Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

Electroencephalogram is a useful interface system that translates the human electrical brain activity into voltage signals. By these means, the recorded brain waves can be employed to characterize, classify, or diagnose mental disorders. A novel neural network model to classify patients with schizophrenia based on electroencephalograms is presented. The proposed model decomposes the multichannel electroencephalogram records into a group of multivariate novel radial basis functions using a fuzzy means algorithm. The decomposition permits to extract different electroencephalogram channel information and distinguish between two sort of classes i.e., schizophrenic patients and healthy controls. Results show improved accuracy compared to classical algorithms reported in the literature i.e., Support Vector Machine, Bayesian Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbour or Adaboost. As a result, the method presented in this paper achieves the highest balanced accuracy, recall, precision and F1 score values, close to 93 % in all cases. The model presented in this paper may be integrated in real time tools involved during the diagnostic of schizophrenia.

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Accurate neural network classification model for schizophrenia disease based on electroencephalogram data

Luján

Sotos²,

Santos³

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic

Parra

Torres

Sotos

et al. 2023

IRBM

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Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases

Bazdar

Hatamian

Ostadieh

et al. 2023

Journal of Medical Signals &Amp; Sensors

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It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.

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Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

Cited by 4 publications

References 23 publications

Accurate neural network classification model for schizophrenia disease based on electroencephalogram data

Accurate neural network classification model for schizophrenia disease based on electroencephalogram data

Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases

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