Denoising ECG Signal by CSTFM Algorithm: Monitoring During Motorbike and Car Races

mHealth technology, by using habitual devices, i.e., smartphones, improves prevention, diagnosis, treatment, monitoring, and management of health. Monitoring heart profile during intense sports activities allows to diagnose pathologies, not identifiable with the traditional Holter approach and, therefore, it can help preventing possible injuries. On the other hand, denoising and extracting features from electrocardiographic (ECG) signal acquired during physical activity is a challenging task due to motion artifacts and measurement noise. In this paper, we propose a solution enabling a complete analysis of ECG signal through the implementation of a robust denoising algorithm, which has been characterized on synthetic signals and then has been tested on real traces acquired with a low-cost smartphone-based device during motorbike and car races.

I. Introduction

High levels of training or high-performance sports entail a high degree of stress for the human heart that could lead to abnormal behavior in some athletes. For these reasons, monitoring the heart profile during sports competitions or high-level physical activities might diagnose pathologies and prevent possible injuries. Wearable mHealth—mobile health—solutions based on the use of smartphones or other portable devices [1] have been adopted in this context to improve prevention, diagnosis, treatment, monitoring, and management of health and lifestyle [2]. In particular, mHealth applications represent an interesting low-cost solution for monitoring cardiac conditions during sport competitions [3], [4].