Personalized characterization of emotional states in patients with bipolar disorder

Abstract

There is strong clinical evidence from the current literature that certain psychological and physiological indicators are closely related to mood changes. However, patients with mental ill-nesses who present similar behavior may be diagnosed differently, which is why a personalized study of each patient is necessary. Following previous promising results in the detection of depres-sion, in this work, supervised machine learning (ML) algorithms were applied to classify the differ-ent states of patients diagnosed with bipolar depressive disorder (BDD). The purpose of this study was to provide relevant information to medical staff and patients’ relatives in order to help them make decisions that may lead to a better management of the disease. The information used was collected from BDD patients through wearable devices (smartwatches), daily self-reports, and med-ical observation at regular appointments. The variables were processed and then statistical tech-niques of data analysis, normalization, noise reduction, and feature selection were applied. An in-dividual analysis of each patient was carried out. Random Forest, Decision Trees, Logistic Regres-sion, and Support Vector Machine algorithms were applied with different configurations. The re-sults allowed us to draw some conclusions. Random Forest achieved the most accurate classifica-tion, but none of the applied models were the best technique for all patients. Besides, the classifica-tion using only selected variables produced better results than using all available information, though the amount and source of the relevant variables differed for each patient. Finally, the smart-watch was the most relevant source of information.