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A Machine Learning-Based Method to Identify Bipolar Disorder Patients

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Abstract

Bipolar disorder is a serious psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Due to the high percentage of people suffering from severe bipolar and depressive disorders, the modelling, characterisation, classification and diagnostic analysis of these mental disorders are of vital importance in medical research. Electroencephalogram (EEG) records offer important information to enhance clinical diagnosis and are widely used in hospitals. For this reason, EEG records and patient data from the Virgen de la Luz Hospital were used in this work. In this paper, an extreme gradient boosting (XGB) machine learning (ML) method involving an EEG signal is proposed. Four supervised ML algorithms including a k-nearest neighbours (KNN), decision tree (DT), Gaussian Naïve Bayes (GNB) and support vector machine (SVM) were compared with the proposed XGB method. The performance of these methods was tested implementing a standard 10-fold cross-validation process. The results indicate that the XGB has the best prediction accuracy (94%), high precision (\(>0.94\)) and high recall (\(>0.94\)). The KNN, SVM, and DT approaches also present moderate prediction accuracy (\(>87\)), moderate recall (\(>0.87\)) and moderate precision (\(>0.87\)). The GNB algorithm shows relatively low classification performance. Based on these results for classification performance and prediction accuracy, the XGB is a solid candidate for a correct classification of patients with bipolar disorder. These findings suggest that XGB system trained with clinical data may serve as a new tool to assist in the diagnosis of patients with bipolar disorder.

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Availability of data and material

The datasets generated and/or analysed during the present study are not publicly available because the patients have not given permission for these data to be openly published. They have only given permission for publication of the results, but they are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was sponsored by Virgen de la Luz Hospital of Cuenca (Spain) and Institute of Technology (University of Castilla-La Mancha).

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Authors and Affiliations

  1. Neurobiological Research Group. Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain

    J. Mateo-Sotos & A. M. Torres

  2. Clinical Psichiatricy Service, Virgen de la Luz Hospital, Cuenca, Spain

    J. L. Santos

  3. School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm, Sweden

    O. Quevedo

  4. University of Bremen, Bremen, Germany

    C. Basar

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  1. J. Mateo-Sotos
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The code generated and/or analysed during the present study are not publicly available because the patients have not given permission for these code to be openly published. They have only given permission for publication of the results, but they are available from the corresponding author upon reasonable request.

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Mateo-Sotos, J., Torres, A.M., Santos, J.L. et al. A Machine Learning-Based Method to Identify Bipolar Disorder Patients. Circuits Syst Signal Process 41, 2244–2265 (2022). https://doi.org/10.1007/s00034-021-01889-1

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