Abstract
Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software.
Highlights
Electroencephalogram (EEG) signals can reveal a great variety of brain pathologic, behavioural, and medication patterns, providing a valuable aid in clinical applications, for instance in early diagnosis, treatment, rehabilitation, and classification [1,2,3,4]
We focused on radial basis function (RBF) neural networks [40,41,42]
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Summary
Electroencephalogram (EEG) signals can reveal a great variety of brain pathologic, behavioural, and medication patterns, providing a valuable aid in clinical applications, for instance in early diagnosis, treatment, rehabilitation, and classification [1,2,3,4]. This study was based on the use of deep learning techniques These techniques have been implemented for years in different topics, for example, classification networks, medical research, or pattern recognition [36,37,38,39]. Not many uses of the RBF architecture have been reported in the literature in bipolar diseases classification It employs radial basis functions as activation functions where the output is a linear combination of RBFs of the inputs and neuron weights. This type of network has some characteristics that make it ideal for this work. The proposed method was compared with different machine learning (ML) techniques for classification.
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