TRUE SCAN NEXUS: A Multi-Parametric Polygraph System Integrating Physiological and Biochemical Signals with Artificial Intelligence

Traditional polygraph systems suffer from significant limitations due to their reliance on a narrow set of physiological indicators such as heart rate, blood pressure, respiration, and skin conductance. These systems are prone to false positives and negatives, raising concerns regarding their scientific credibility and legal admissibility. To address these shortcomings, the TRUE SCAN NEXUS system was developed as a next-generation polygraph that integrates both biochemical and physiological parameters with advanced artificial intelligence algorithms. The system simultaneously monitors cortisol, lactate, catecholamines, oxytocin, lactate dehydrogenase, and electrolytes, in addition to conventional physiological markers including heart rate variability, blood pressure, respiration rhythm, ocular dynamics, and body temperature. Data are acquired through electrochemical, enzymatic, optical, and thermal sensors, processed via clustering and machine learning methods, and analyzed with deep learning architectures such as LSTM and CNN for real-time classification. The integration of multi-modal signals significantly improves accuracy and robustness, while explainability techniques such as SHAP and LIME enhance transparency. Preliminary evaluations demonstrate the potential of this system to overcome the shortcomings of traditional polygraphs and expand applications into stress monitoring, psychological assessment, and clinical practice.

کلیدواژه ها:

Polygraph ، Stress Detection ، Cortisol ، Biosensors ، Artificial Intelligence ، Machine Learning ، Physiological Signals ، Multi-Parametric System