Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

Journal: Computers in biology and medicine
Published Date: Sep 1, 2019

Abstract

BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from healthy controls was developed based on linear and nonlinear heart rate variability (HRV), which reflects the autonomic cardiovascular regulation.

Authors

  • Sangwon Byun
    Department of Electronics Engineering, Incheon National University, Incheon, Republic of Korea.
  • Ah Young Kim
    Medical Information Research Section, Electronics and Telecommunications Research Institute, Dajeon, Republic of Korea.
  • Eun Hye Jang
    Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), 34129, Daejeon, South Korea.
  • Seunghwan Kim
    Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), 34129, Daejeon, South Korea.
  • Kwan Woo Choi
    Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06351, Seoul, South Korea; Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, 02841, South Korea.
  • Han Young Yu
    Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), 34129, Daejeon, South Korea. Electronic address: uhan0@etri.re.kr.
  • Hong Jin Jeon
    Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

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