Citation

Zanella-Calzada LA, Galván-Tejada CE, Chávez-Lamas NM, Gracia-Cortés MDC, Magallanes-Quintanar R, Celaya-Padilla JM, Galván-Tejada JI, Gamboa-Rosales H. Diagnostics (Basel) 2019; 9(1): e9010008.

Affiliation

Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico. hamurabigr@uaz.edu.mx.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/diagnostics9010008

PMID

30634621

Abstract

Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients; nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the "Depresjon" database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier.

RESULTS show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.

Language: en

Keywords

classification; depresjon database; depression; feature extraction; motor activity; random forest