Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device

Abstract

Objectives: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies. Methods: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study. Results: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively. Conclusions: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.

Keywords: AcuPebble; digital health; respiratory rate; validation study; wearable technology.

Figure 1

Model (not patient) wearing the sensor.

Figure 2

Flow of participants and data sufficiency diagram.

Figure 3

Linear regression of the complete prospective study dataset, showing comparison between the test and reference devices during guided breathing (blue) and artifacts (red).

Figure 4

Bland–Altman plot showing agreement between the test and reference devices for the complete prospective study, with artifacts shown in red.

Figure 5

Linear regression of the guided breathing phase of the prospective study showing comparison between the test and reference devices.

Figure 6

Bland–Altman plot showing agreement between the test and reference devices for the guided breathing phase of the prospective study.

Figure 7

Bland–Altman plot showing the agreement for all the subjects in the retrospective validation.