Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1924
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dc.contributor299983es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7635-4687-
dc.contributor.otherhttps://orcid.org/0000-0002-9498-6602-
dc.contributor.other0000-0002-9498-6602-
dc.coverage.spatialGlobales_ES
dc.creatorZanella Calzada, Laura Alejandra-
dc.creatorGalván Tejada, Carlos Eric-
dc.creatorChávez Lamas, Nubia-
dc.creatorGracia Cortés, Ma. del Carmen-
dc.creatorMagallanes Quintanar, Rafael-
dc.creatorCelaya Padilla, José-
dc.creatorGalván Tejada, Jorge-
dc.creatorGamboa Rosales, Hamurabi-
dc.date.accessioned2020-05-20T18:43:25Z-
dc.date.available2020-05-20T18:43:25Z-
dc.date.issued2019-03-10-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn2075-4418es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1924-
dc.identifier.urihttps://doi.org/10.48779/3bs0-mg76-
dc.description.abstractDepression 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.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationhttps://www.mdpi.com/2075-4418/9/1/8es_ES
dc.relation.urigeneralPublices_ES
dc.sourceDiagnostics, Vol. 9, No. 1, 2019es_ES
dc.subject.classificationMEDICINA Y CIENCIAS DE LA SALUD [3]es_ES
dc.subject.otherDepressiones_ES
dc.subject.otherdepresjon databasees_ES
dc.subject.othermotor activityes_ES
dc.subject.otherfeature extractiones_ES
dc.subject.otherclassificationes_ES
dc.subject.otherrandom forestes_ES
dc.titleFeature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patientses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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