Integration of Biochemical and Physiological Sensors in the TRUE SCAN NEXUS: Towards a Multi-Parametric AI-Assisted Polygraph

Conventional polygraphs are limited by their reliance on a small set of physiological signals, which undermines both accuracy and scientific credibility. The TRUE SCAN NEXUS system addresses these limitations by integrating a wide range of biochemical and physiological parameters into a multi-parametric framework supported by machine learning algorithms. The device simultaneously monitors biomarkers such as cortisol, lactate, catecholamines, oxytocin, lactate dehydrogenase (LDH), and electrolytes, along with physiological indicators including heart rate variability, blood pressure, respiratory rhythm, ocular dynamics, and thermoregulation. Data are acquired through electrochemical, enzymatic, optical, and thermal sensors, preprocessed with noise reduction and normalization methods, and analyzed using clustering and AI models such as LSTM and CNN. Comparative evaluation highlights the superiority of the multi-parametric approach over traditional polygraphs in terms of robustness, resistance to variability, and real-time interpretability.

کلیدواژه ها:

Polygraph ، Cortisol ، Lactate ، Biosensors ، Physiological Monitoring ، Machine Learning ، Stress Detection ، AI