A non-parametric validation framework for photoplethysmography-based heart rate monitoring: a proof-of-concept study using the two-sample Kolmogorov–Smirnov test
Submitted: 2025-10-27
|Accepted: 2025-12-10
|Published: 2026-01-27
Copyright (c) 2025 Javier Cruz-Salgado, Roxana Zaricell Bautista-López, Javier Yañez-Mendiola, Edgar Augusto Ruelas-Santoyo, Israel Miguel-Andrés, José Alfredo Jiménez-García
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
photoplethysmography, two-sample Kolmogorov–Smirnov test, heart rate comparison, biomedical signal processing, non-invasive health monitoring
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Abstract:
This article establishes a non-parametric validation framework for photoplethysmography (PPG) signals intended for heart rate monitoring, formulated as a methodological proof-of-concept study. The study applies the two-sample Kolmogorov–Smirnov test as a robust and versatile method for comparing the distributions of PPG signals. By integrating the two-sample Kolmogorov–Smirnov test into the validation process of cardiac pulse measurement devices, the work demonstrates its effectiveness in enhancing the accuracy and reliability of biomedical signal analysis. Results show that, when comparing signals against calibrated reference devices and visualizing cumulative distribution functions, the two-sample Kolmogorov–Smirnov test is capable of detecting subtle differences in signal behavior. This innovative use of the two-sample Kolmogorov–Smirnov test provides valuable insights for the design and validation of biomedical signal processing systems and contributes to the advancement of non-invasive health monitoring technologies. The proposed framework is evaluated under controlled experimental conditions using data acquired from a single subject. Consequently, the results should be interpreted as a methodological validation of the proposed statistical approach rather than as a definitive clinical validation, and are intended to demonstrate feasibility and analytical relevance rather than population-level generalizability.
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