One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Abstract

Teaching machines to learn patterns in data is very common these days, and it has a broad spectrum of applications everywhere. Sensors like smart-watches are getting more functionality each year, and more and more people buy them. Passing data from the watches, for example, activity or heart rate to machine learning algorithms, can generate significant results within many fields. Mental health is an example of a field where computer-generated predictions can be helpful to gain knowledge about patients. For example, machine learning can help predict that someone has a specific type of mental disorder. In this thesis, we present applied machine learning to detect depression. The dataset (collected for another study about behavioral patterns in schizophrenia vs. major depression) contains motor activity measurements for each minute in the measured period for each participant. Three machine learning models are trained to fit time-sliced segments of these measurements. The first model classifies participants into condition group (depressed) and control group (non-depressed). We trained another model to classify the depression level of participants (normal, mild or moderate). Finally, we trained a model that predicts MADRS scores. We evaluate the performance of classification models using leave one participant out validation as a technique, in which we achieved an average F1-score of 0.70 for detecting control/condition group and 0.30 for detecting the depression levels. The MADRS score prediction resulted in a mean squared error of approximately 4.0. These performance scores are promising, but not good enough to be used in the real world. However, not much more work is needed for the first model if we apply it to a dataset with more participants.

An End-to-End Machine Learning Solution for Anxiety and Depressive Disorder Symptom Occurrence During COVID-19: A New York Case Study

“…Solutions using human voice have been realized to develop automatic mental health monitors assisting in early diagnosis and longitudinal monitoring of anxiety and depression symptoms in everyday speech conversation [22,23,24]. Accelerometers have also been used to detect the presence and level of depression from motor activity recordings [25]. Nguyen et al apply text mining within online social communities to better understand linguistic-related topics in the context of mental health [26].…”

Machine Learning in Mental Health

“…Frogner et al [ 62 ] Accelerometer Detecting symptoms/ condition Development of multiple ML models to detect presence and level of depression from motor activity recordings.…”

“…, for PTSD [ 112 ]. The BiAffect mobile phone and Depresjon dataset were used to access acceleration data of people with depression [ 62 ] and bipolar conditions [ 27 ], while the English Longitudinal Study of Ageing (ELSA) provided psychological and mental health data on older adults as indicators of depression [ 209 ]. Finally, a few papers reported on the re-use of previously collected user data in the context of a commercial wellness platform [ 152 ], for social media analysis [ 64 ], and mood or well-being research (e.g .…”

“…Nevertheless, there may be a need for more explicit dialogue and efforts to nurture a clearer understanding for those from whom data is being collected and analyzed as to what constitutes the purpose of the data analysis, and what risks and benefits sharing their data may entail for the [ 4 ]; Alam et al [ 6 ]; Chang et al [ 31 ]; Chen et al [ 33 ]; DeMasi and Recht [ 44 ]; Diederich et al [ 45 ]; Fatima et al [ 57 ]; Frogner et al [ 62 ]; Galiatsatos et al [ 63 ]; Gjoreski et al [ 67 ]; Joshi et al [ 86 ]; Kavuluru et al [ 89 ]; Mallol-Ragolta et al [ 112 ]; Nguyen et al [ 133 ]; Nosakhare and Picard [ 135 ]; Panagiotakopoulos et al [ 139 ]; Patterson and Cloud [ 144 ]; Pestian et al [ 145 ]; Rastogi et al [ 154 ]; Ray et al [ 155 ]; Spathis et al [ 177 ]; Tavabi [ 184 ]; Tran et al [ 192 ]; Tsiakas et al [ 193 ]; Wilbourne et al [ 205 ] Reports of ethical approval/review exemption Institutional/Regional IRB Doryab et al [ 50 ]; Ernala et al [ 53 ]; Nobles et al [ 134 ]; Paredes et al [ 140 ]; Salekin et al [ 168 ]; Wang et al [ 201 ]; Yazdevar et al [ 211 ]; Zakaria et al [ 217 ] Re-use of data (e.g . , that previously received or was exempt from ethical approval)…”

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

“…Additionally, the use of motor activity in time series has been used to acquire information that helps identify possible cases of depression. Frogner et al [ 25 ] developed a one-dimensional convolutional neural network (1D-CNN) to detect depression through measurements of motor activity, where three models with different time segments and different functions were trained. The first model classifies the participants into a condition group and a control group.…”