Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients

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.

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

“…The search term “depression” identified five studies [ 5 , 15 , 21 , 29 , 30 ] which assessed only depression, and one study [ 24 ] which assessed “mental health” broadly. EEG is a non-obtrusive, electrophysiological measure of the spontaneous electrical activity in the brain and is widely used to study antidepressant treatment responses due to its availability and low cost [ 54 ].…”

“…The remaining two studies [ 17 , 29 ] looked at activity levels for the detection of depression using a wearable actigraph watch or smart watch with or without a smartphone. In the study by Zanella-Calzada et al (2019), real-time measurements of behaviour, feelings, and activity were recorded using an Ecological Momentary Assessment [ 56 ], through use of smart phones and an actigraph watch; specifically, the Actiwatch (Cambridge Neurotechnology Ltd., Cambridge, UK, model AW4) [ 29 ]. This assessment is necessary for depression monitoring, as most depressive symptom monitoring methods rely on patient reports, which are commonly biased.…”

Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

“…It is important to mention that RF was selected for the classification since it has been used to classify the motor activity of depressed subjects in other works. Zanella-Calzada et al [22] present the classification of depressive and no depressive episodes using RF, obtaining an accuracy of 0.893, while M. Pal et al [32] compare the performance between RF and SVM, resulting RF more efficient even with fewer parameters to make the classification.…”

“…For each dataset, the 24 features shown in Table 2 are extracted. This process is based on similar works that extract features from an accelerometer signal [22][23][24].…”

Evaluation of Five Classifiers for Depression Episodes Detection

“…The main purpose of this approach is the development of tools that allow to automatically identify when a patient presents depression from certain type of information provided by the patient. To be able to carry out this task, the AI tools are based on a previous training, which consists in submitting an algorithm to an automatic learning that consists in the search of relationships of a considerable number of examples of cases similar to those that they want to classify, with the real diagnosis of those examples, being information of patients with depression specifically for this case [14].…”