Slow paced-breathing has recently gained increasing recognition for its potential to improve an individual’s health and overall well being. Specifically, it has been shown that such breathing can significantly lower the blood pressure (Schein et al., 1991; Grossman et al., 2001; Rosenthal et al., 2001; van Hateren et al., 2014; Elliot et al., 2004; Meles et al., 2004; Landman et al., 2014; Schein et al., 2009; Altena et al., 2009), lower neural sympathetic activity (Oneda el at., 2010; Harada et al., 2014; Barros et al., 2014), decrease peripheral resistance (Gavish et al., 2011; Ovadia-Blechman et al., 2017; Faconti et al., 2019; Bachler et al., 2020) and arterial stiffness (Parati et al., 2008; Ekman et al., 2011; Bilo et al., 2012; Debicka-Dabrowska et al., 2015) as well as reduce anxiety and stress (Morarend et al., 2011; Ouseph et al., 2014; Gabriely et al., 2020; ). There are many devices that can guide slow breathing (Miri et al., 2020; Martin et al., 2014; Kirby et al., 2009). However, it is important to monitor that the user is actually following the guided breathing pattern (Benetazzo et al., 2016), as it is common for people to get distracted and deviate from the breathing rate of the breath pacer.
In this light, several existing techniques, that used image (Nam et al., 2016), sound (Nam et al., 2015; Avalur et al., 2013) or motion recognition (Hernandez et al., 2015; Shen et al., 2017; Prigent et al., 2021), have been used in order to determine the breathing rate of the person. Specifically, such techniques as extracting breathing rate from image recognition (Benetazzo et al., 2016, Nam et al., 2016) have demonstrated outstanding performance. For example, there exists an FDA-approved device, RESPeRATE that can detect a person's breathing rate using a chest strap (Anderson et al., 2009). Such data then can be used to judge if the person’s breathing is compliant with the breath pacer during a meditation session (Zhang et al., 2013).
In this paper we explored the possibility of using pulse rate variability to determine compliance with a breath pacer. Recently, a novel technique to calculate the magnitude of pulse rate variability has been developed known as Dynamic Phase Extraction (DPE) (Li et al., 2022). DPE calculates how much the pulse rate of a person varies during a single breath due to respiratory sinus arrhythmia as well as its phase shift relative to breath (Fig. 1). The magnitude drops significantly (Fig. 3a) as a person stops following the breath pacer and starts breathing at their own rate. Therefore, in this study we explored the possibility of using Dynamic Phase Extraction and change in magnitude of the pulse rate variability in order to determine the compliance with the breath pacer.