Respiratory rate estimation from the oscillometric waveform obtained from a non-invasive cuff-based blood pressure device

The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeter's photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors, and may be used to estimate the vital sign respiratory rate (RR). We propose a novel method that combines the information from the two respiratory-induced variations (frequency and amplitude) via frequency analysis to both estimate RR and eliminate estimations considered to be unreliable because of poor signal quality. The method was evaluated using data acquired from 40 subjects containing ECG, respiration and blood pressure waveforms, the latter acquired using an in-house built blood pressure device that is able to connect to a mobile phone. Results demonstrated a good RR estimation accuracy of our method when compared to the reference values extracted from the reference respiration waveforms (mean absolute error of 2.69 breaths/min), which is comparable to existing methods in the literature that extract RR from other physiological signals. The proposed method has been implemented in Java on the Android device for use in an mHealth platform.