Voltammetric Data for Cardiac Biomarker Detection via Screen-Printed Electrodes

An algorithm-assisted analytical framework for automated interpretation of voltammetric data from screen-printed carbon electrode potentiostats. The dataset, created by Wikan Danar Sunindyo and last updated in May 2026, contains processed electrochemical signals for detecting cardiac biomarkers. It includes results from applying polynomial fitting and asymmetric least squares methods for baseline correction and peak detection.

Use Cases

• Benchmarking automated peak detection algorithms based on cyclic and differential pulse voltammetry signals.
• Developing baseline correction methods for electrochemical data based on polynomial fitting and asymmetric least squares techniques.
• Training models to distinguish biomarker concentration levels based on peak-to-baseline current response.
• Improving reproducibility in electrochemical biosensor measurements by reducing manual interpretation.

Strengths

• Data is validated against reference electrochemical analysis software, demonstrating reliable peak detection.
• The dataset is small (5.5 KB), facilitating quick download and initial inspection.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's scope is limited to 5.5 KB, suggesting a small sample size or processed summary data.

Provenance

Source

figshare

Collection Method

Data likely originates from electrochemical voltammetry experiments using screen-printed carbon electrodes.

Freshness

Last updated 2026-05-13 17:49:22