Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study.

ABSTRACT:

Background

Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks.

Objective

The aim of this study was to impute missing values in data using a deep learning approach.

Methods

To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our

SUBMITTER: Jang JH 

PROVIDER: S-EPMC7413283 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature