Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm.

Journal: Progress in neuro-psychopharmacology & biological psychiatry
Published Date: Feb 20, 2017

Abstract

OBJECTIVE: Major depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and the autonomic nervous system. This study applied a machine-learning method to classify MDD and control groups by incorporating data from serum proteomic analysis and heart rate variability (HRV) analysis for the identification of novel peripheral biomarkers.

Authors

  • Eun Young Kim
    Department of Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Min Young Lee
    Institute for Systems Biology, Seattle, WA, United States; Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yongin, Republic of Korea.
  • Se Hyun Kim
    Department of Psychiatry, Dongguk University International Hospital, Goyang, Republic of Korea.; Institute of Clinical Psychopharmacology, Dongguk University College of Medicine, Goyang, Republic of Korea.
  • Kyooseob Ha
    Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
  • Kwang Pyo Kim
    Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yongin, Republic of Korea.
  • Yong Min Ahn
    Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: aym@snu.ac.kr.

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