Depression Level Classification Using Machine Learning Classifiers Based on Actigraphy Data

Depression Level Classification Using Machine Learning Classifiers Based on Actigraphy Data

Estimating and classifying depression status are critical in the clinical and psychological domains to map the course of treatment. Prior researchers used biosignal time-series data to reflect the variation in factors associated with depression. In addition, machine learning algorithms were applied...

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Bibliographic Details
Main Authors: Jung-Gu Choi, Inhwan Ko, Sanghoon Han
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Accelerometer-based actigraphy data
classification algorithm
circadian rhythm
depression level
machine learning
multi-level classification
Online Access:https://ieeexplore.ieee.org/document/9514893/

Internet

https://ieeexplore.ieee.org/document/9514893/

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