Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Journal: Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Published Date: Feb 8, 2018

Abstract

BACKGROUND: Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods.

Authors

  • Bosun Hwang
    1 Department of Computer Science and Engineering, Seoul National University , Seoul, Korea.
  • Jiwoo You
    2 Department of Computer Engineering, Kwangwoon University , Nowon-gu, Seoul, Korea.
  • Thomas Vaessen
    3 KU Leuven, Department of Neurosciences, Center for Contextual Psychiatry , Leuven, Belgium .
  • Inez Myin-Germeys
    3 KU Leuven, Department of Neurosciences, Center for Contextual Psychiatry , Leuven, Belgium .
  • Cheolsoo Park
    2 Department of Computer Engineering, Kwangwoon University , Nowon-gu, Seoul, Korea.
  • Byoung-Tak Zhang
    School of Computer Science and Engineering, Seoul National University, Seoul 08826, South Korea; Interdisciplinary Program in Neuroscience, Seoul National University, Seoul 08826, South Korea; Surromind Robotics, 1 Gwanak-ro Gwanak-gu, Seoul 08826, South Korea. Electronic address: btzhang@bi.snu.ac.kr.

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