Formulation of a novel HRV classification model as a surrogate fraudulence detection schema


  • Tan Tian Swee Universiti Teknologi Malaysia
  • Kelvin Ling Chia Hiik
  • Tan Jia Hou
  • Leong Kah Meng
  • Mohammed Rafiq Abdul-Kadir
  • Arief Ruhullah A. Harris
  • Muhamad Firdaus Mohd Rafi
  • Leo Bodey
  • Yii Cheng Tay
  • Azli Yahya
  • Joyce Sia Sin Yin
  • Matthias Tiong Foh Thye
  • Tengku Ahmad Iskandar Tengku Alang
  • Sameen Ahmed Malik



Heart Rate Variability (HRV), lie detection, classification model


Lie detection has been studied since a few decades ago, usually for the purpose of producing a scheme to assist in the investigation of identifying the culprit from a list of suspects. Heart Rate Variability (HRV) may be used as a method in lie detection due to its versatility and suitability. However, since its analysis is not instantaneous, a new experiment is described in this paper to overcome the problem. Additionally, a preliminary HRV classification model is designed to further enhance the classification model which is able to distinguish the lie from the truth for up to 80%.

Author Biography

Tan Tian Swee, Universiti Teknologi Malaysia

Tan Tian Swee is working as Senior Lecturer and Program Manager in the Faculty of Biomedical Engineering, UTM. He received both his M.Sc. degree and Doctorate degree back in the year 2004 and 2008 respectively from the Universiti Teknologi Malaysia.  His research area encompasses the area of Digital Signal Processing and has published numerous high impact factor journals. He is a member of the Medical Device and Technology Group (MediTEG) and Frontier Materials research alliances


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