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Open AccessArticle

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

1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
2
LORIA, Université de Lorraine, Campus Scientifique BP 239, 54506 Nancy, France
3
CONACYT, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Diagnostics 2020, 10(3), 162; https://doi.org/10.3390/diagnostics10030162
Received: 30 December 2019 / Revised: 14 February 2020 / Accepted: 17 February 2020 / Published: 17 March 2020
(This article belongs to the Special Issue Mobile Diagnosis 2.0)
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in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient.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. View Full-Text
Keywords: motor activity; depression; depressive episodes; data mining; random forest; night motor activity; depression; depressive episodes; data mining; random forest; night
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Rodríguez-Ruiz, J.G.; Galván-Tejada, C.E.; Zanella-Calzada, L.A.; Celaya-Padilla, J.M.; Galván-Tejada, J.I.; Gamboa-Rosales, H.; Luna-García, H.; Magallanes-Quintanar, R.; Soto-Murillo, M.A. Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes. Diagnostics 2020, 10, 162.

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