ArticleDiagnostics (Basel, Switzerland)2020
Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.Scientific reports · 2025Article
- Convolutional Neural Network for Depression and Schizophrenia Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Depression Recognition Using Daily Wearable-Derived Physiological Data.Sensors (Basel, Switzerland) · 2025Article
- Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource.Psychology research and behavior management · 2024Article
- Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.NPJ digital medicine · 2023Article
- Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.Journal of medical Internet research · 2023Article
- Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset.Bioengineering (Basel, Switzerland) · 2022Article
- Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal.Healthcare (Basel, Switzerland) · 2022Article
- Internet of things issues related to psychiatry.International journal of bipolar disorders · 2021Review
- Mobile Diagnostic Devices for Digital Transformation in Personalized Healthcare.Diagnostics (Basel, Switzerland) · 2020Article
- Univariate Analysis of Short-Chain Fatty Acids Related to Sudden Infant Death Syndrome.Diagnostics (Basel, Switzerland) · 2020Article
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Authors and funding
9 authors.
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Abstract
Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.
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