A Signal Quality Index for Improving the Estimation of Breath-by-Breath Respiratory Rate During Sport and Exercise

Abstract

The breath-by-breath measurement of respiratory rate (RR) plays a pivotal role in sports and exercise. The accurate estimation of RR values on a breath-by-breath basis with wearable sensors has several open challenges during training, including motion artifacts and other breathing-unrelated events. This article presents a novel method based on a signal quality index (SQI) for identifying and excluding unreliable breaths from breathing waveforms. The method analyses the morphological characteristics of the respiratory signal, comparing each breath with an average breath template calculated as an average of all individual breaths. The comparison is made using a template matching without the need of a reference signal. Experimental tests have been carried out at rest and during walking, running, and cycling activities to assess the method's performance in estimating breath-by-breath RR by comparison with reference values collected with a flowmeter. The comparison between the RR values has been performed with an ad hoc developed method able to accomplish this task, even when the number of breaths identified by the two devices is different. The obtained results showed that our SQI-based method improves the accuracy of RR estimation by reducing the mean absolute percentage error (MAPE) values in all the tested conditions (18.5%, 22.2%, 2.8%, and 14.1% of MAPE improvement rate during rest, walking, running and cycling, respectively). Pilot tests during high-intensity interval training (HIIT) also demonstrated a 30.7% MAPE improvement rate. The promising findings demonstrated that using SQI-based algorithms can lead to more accurate RR estimations during exercise by using comfortable wearable sensors.