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

Author:

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

Abstract

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%.

Publisher

Penerbit UTM Press

Subject

General Physics and Astronomy,General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Mathematics,General Chemistry

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. LSTM Model for Brain Control Interface Based-Lie Detection;2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC);2024-02-05

2. Validity of the Technique for the Concealed Information Revealing Based on the Registration of Heart Rate during Polygraph Testing;Experimental Psychology (Russia);2023-04-21

3. Using Neural Network Models for BCI Based Lie Detection;2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON);2022-10-26

4. Implementation of Machine Learning in BCI Based Lie Detection;2022 IEEE World AI IoT Congress (AIIoT);2022-06-06

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