In this paper, different techniques based on deep learning algorithms used for the classification and diagnosis of patients with mental disorders i.e., schizophrenia and bipolar disorder, are presented. To this aim, the signals obtained from 32 unipolar electrodes of non-invasive electroencephalogram analysis are studied to obtain its main features. More specifically, the analysis performed utilizes an innovative radial basis function neural network based on fuzzy means algorithm. Furthermore, the analysis of the variance of statistical parameters and entropy is applied. In total, 312 subjects with schizophrenia and 105 patients with bipolar disorder have been evaluated. The results obtained show a correct classification in patients compared to healthy controls. The proposed methods achieved a better performance than other machine learning techniques such as support vector machine or k-nearest neighbour, with an accuracy close to 96%. It can be concluded that this type of classifications will allow the training of algorithms that can be used to identify and classify different mental disorders with very high accuracy.

In this paper, different techniques based on deep learning algorithms used for the classification and diagnosis of patients with mental disorders i.e., bipolar disorder, are presented. To this aim, the potentials obtained from 32 unipolar electrodes of non-invasive electroencephalogram analysis are studied to obtain its main features. More specifically, the analysis performed utilizes an innovative radial basis function neural network based on fuzzy means algorithm. Furthermore, the analysis of the variance to statistical parameters and entropy is applied. In total, 105 patients with bipolar disorder and 205 control subjects have been evaluated. The results obtained shows a correct classification in patients with bipolar disorder compared to healthy controls. The proposed methods achieved a better performance than other machine learning techniques such as support vector machine or k-nearest neighbour, with an accuracy close to 96%. It can be concluded that this type of classifications will allow the training of algorithms that can be used to identify and classify different mental disorders with very high accuracy.