Z-Normalized Euclidean Distance-Based Approach for Heart Rate Estimation and Artifact Identification in PPG Signals

Photoplethysmography (PPG) is increasingly used for monitoring cardiological vital parameters, particularly heart rate (HR), due to its noninvasiveness and low cost. However, HR estimation is often challenging due to undesired artifacts affecting the PPG signal. This paper addresses this issue by presenting a technique based on the Euclidean distance of z -normalized sequences within the well-known Matrix Profile (MP) framework. The proposed MP-based approach provides a dual benefit: it enables HR estimation while assessing signal quality by detecting anomalies in the PPG data. The focus is on its application to short time series, with a future perspective of making it suitable for real-time HR monitoring.