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

• Main Authors: Tan, Tian Swee, Kelvin, Ling Chia Hiik, Tan, Jia Hou, Leong, Kah Meng, Abdul-Kadir, Mohammed Rafiq, A. Harris, Arief Ruhullah, Mohd. Rafi, Muhamad Firdaus, Leo, Bodey, Yii, Cheng Tay, Yahya, Azli, Joyce, Sia Sin Yin, Matthias, Tiong Foh Thye, Tengku Alang, Tengku Ahmad Iskandar, Malik, Sameen Ahmed
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2020
• Subjects: QH301 Biology
• Online Access: http://eprints.utm.my/id/eprint/85579/1/AzliYahya2020_FormulationofaNovelHRVClassification.pdf; http://eprints.utm.my/id/eprint/85579/; https://mjfas.utm.my/index.php/mjfas/article/view/1141

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%.