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

Journal: JMIR mHealth and uHealth
Published Date: Jul 23, 2020

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.

Authors

  • Jong-Hwan Jang
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
  • Junggu Choi
    Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, Gyeonggi-do, Republic of Korea.
  • Hyun Woong Roh
    Department of Brain Science, School of Medicine, Ajou University, Suwon, Gyeonggi-do, Republic of Korea.
  • Sang Joon Son
    Department of Psychiatry, School of Medicine, Ajou University, Suwon, Gyeonggi-do, Republic of Korea.
  • Chang Hyung Hong
    Department of Psychiatry, School of Medicine, Ajou University, Suwon, Gyeonggi-do, Republic of Korea.
  • Eun Young Kim
    Department of Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Tae Young Kim
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
  • Dukyong Yoon
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.

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