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Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal as Information Source
المؤلفون المشاركون
Gamboa-Rosales, Hamurabi
Galván-Tejada, Carlos E.
Galván-Tejada, Jorge I.
Zanella-Calzada, Laura A.
Celaya-Padilla, José M.
Magallanes-Quintanar, Rafael
Chávez-Lamas, Nubia M.
Gracia-Cortés, Ma. del Carmen
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-04-23
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life.
Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences.
Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used.
These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches.
In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database.
Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done.
Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic.
Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC.
These results allow us to conclude that using the activity signal from a smartband, it is possible to distinguish between depressive states, providing a preliminary and automated tool to specialists for the diagnosis of depression almost in real time.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Galván-Tejada, Carlos E.& Zanella-Calzada, Laura A.& Gamboa-Rosales, Hamurabi& Galván-Tejada, Jorge I.& Chávez-Lamas, Nubia M.& Gracia-Cortés, Ma. del Carmen…[et al.]. 2019. Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal as Information Source. Mobile Information Systems،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1193894
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Galván-Tejada, Carlos E.…[et al.]. Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal as Information Source. Mobile Information Systems No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1193894
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Galván-Tejada, Carlos E.& Zanella-Calzada, Laura A.& Gamboa-Rosales, Hamurabi& Galván-Tejada, Jorge I.& Chávez-Lamas, Nubia M.& Gracia-Cortés, Ma. del Carmen…[et al.]. Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal as Information Source. Mobile Information Systems. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1193894
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193894
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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