Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset

Abstract

Depression is a common illness worldwide, affecting an estimated 3.8% of the population, including 5% of all adults, in particular, 5.7% of adults over 60 years of age. Unfortunately, at present, the ways to evaluate different mental disorders, like the Montgomery–Åsberg depression rating scale (MADRS) and observations, need a great effort, on part of specialists due to the lack of availability of patients to obtain the necessary information to know their conditions and to detect illness such as depression in an objective way. Based on data analysis and artificial intelligence techniques, like Convolutional Neural Network (CNN), it is possible to classify a person, from the mental status examination, into two classes. Moreover, it is beneficial to observe how the data of these two classes are similar in different time intervals. In this study, a motor activity database was used, from which the readings of 55 subjects of study (32 healthy and 23 with some degree of depression) were recorded with a small wrist-worn accelerometer to detect the peak amplitude of movement acceleration and generate a transient voltage signal proportional to the rate of acceleration. Motor activity data were selected per patient in time-lapses of one day for seven days (one week) in one-minute intervals. The data were pre-processed to be given to a two-dimensional convolutional network (2D-CNN), where each record of motor activity per minute was represented as a pixel of an image. The proposed model is capable of detecting depression in real-time (if this is implemented in a mobile device such as a smartwatch) with low computational cost and accuracy of 76.72% In summary, the model shows promising abilities to detect possible cases of depression, providing a helpful resource to identify the condition and be able to take the appropriate follow-up for the patient.

Bovine colostrum supplementation as a new perspective in depression and substance use disorder treatment: a randomized placebo-controlled study

“…GRA, granulocytes; HCT, hematocrit; HGB, hemoglobin; LYM, lymphocytes; MCH, mean corpuscular hemoglobin mass; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MON, monocytes; MPV, mean platelet volume; PCT, platelet hematocrit; PDW, platelet distribution width; PLT, platelet count; PLCR, platelet large cell ratio; RBC, red blood cells; RDW-C, red blood cells distribution widthcoefficient of variation; RDW-S, red blood cells distribution widthstandard deviation; WBC, white blood cells. 1 The data were analyzed with one-way analysis of variance with repeated measurements; the effect size is expressed as partial eta-squared (h 2 p ). 2 The data were analyzed with the Wilcoxon signed-rank test; the effect size is expressed as the rank correlation coefficient (r c ).…”

“…Abbreviations: DOP, dopamine; SER, serotonin. 1 The data were analyzed with one-way analysis of variance with repeated measurements; the effect size is expressed as partial eta-squared (h 2 p ). 2 The data were analyzed with the Wilcoxon signed-rank test; the effect size is expressed as the rank correlation coefficient (r c ).…”

“…confidence interval (box), 95% CI + one standard deviation (whisker), and raw data. 1 The data were analyzed with one-way analysis of variance with repeated measurements. 2 The data were analyzed with the Wilcoxon signed-rank test.…”

Feature Selection of Motor Activity in Intervals of Time with Genetics Algorithms for Depression Detection

“…Motor Activity Time Series of Depresjon Dataset [30] . As evident from the data, a significant distinction between the two cases can be observed, with a notice- Samples collected with Actiwatch from both a non-depressed and a depressed subject of the Depresjon database [30] .…”

“…Motor Activity Time Series of Depresjon Dataset [30] . As evident from the data, a significant distinction between the two cases can be observed, with a notice- Samples collected with Actiwatch from both a non-depressed and a depressed subject of the Depresjon database [30] . 1 shows the results of their confusion matrices which is often applied in machine learning to evaluate or visualize the model behavior in supervised classification scenarios [31] .…”