Algorithm-assisted interpretation of cyclic and differential pulse voltammetry for cardiac troponin detection.

Abstract

Cardiovascular disease remains a leading cause of mortality worldwide, and rapid identification of cardiac biomarkers is essential for early detection. Electrochemical voltammetry techniques, particularly cyclic voltammetry (CV) and differential pulse voltammetry (DPV), are widely used for detecting cardiac troponin; however, interpretation of raw voltammetric signals is often affected by baseline drift, signal noise, and operator-dependent analysis. This study proposes an algorithm-assisted analytical framework for automated interpretation of voltammetric data obtained from a screen-printed carbon electrode potentiostat. Polynomial fitting was applied for baseline correction in CV signals, while asymmetric least squares (ALS) was employed for DPV data. Peak-to-baseline current response was extracted as a quantitative indicator of biomarker presence. The proposed method successfully identified characteristic voltammetric peaks and distinguished samples with higher and lower cardiac biomarker responses relative to a predefined detection threshold. The analysis showed close agreement with reference electrochemical analysis software, demonstrating reliable peak detection and baseline estimation. By reducing manual interpretation and improving signal clarity, the framework enhances the reproducibility and accessibility of electrochemical biosensor measurements and supports early screening of cardiac biomarkers.