Performance of cough monitoring by Albus Home, a contactless and automated system for nocturnal respiratory monitoring at home

TY - JOUR

T1 - Performance of cough monitoring by Albus Home, a contactless and automated system for nocturnal respiratory monitoring at home

AU - Do, William

AU - Russell, Richard

AU - Wheeler, Christopher

AU - Javed, Hamza

AU - Dogan, Cihan

AU - Cunningham, George

AU - Khanna, Vikaran

AU - Devos, Maarten

AU - Satia, Imran

AU - Bafadhel, Mona

AU - Pavord, Ian

N1 - Funding Information: Support statement: This work was funded by Albus Health (registered BreatheOx Limited). I. Satia is currently supported by the E.J. Moran Campbell Early Career Award, Department of Medicine, McMaster University. Funding information for this article has been deposited with the Crossref Funder Registry. Publisher Copyright: © The authors or their employers 2022.

PY - 2022/10/1

Y1 - 2022/10/1

N2 - Introduction Objective cough frequency is a key clinical end-point but existing wearable monitors are limited to 24-h recordings. Albus Home uses contactless motion, acoustic and environmental sensors to monitor multiple metrics, including respiratory rate and cough without encroaching on patient lifestyle. The aim of this study was to evaluate measurement characteristics of nocturnal cough monitoring by Albus Home compared to manual counts. Methods Adults with respiratory conditions underwent overnight monitoring using Albus Home in their usual bedroom environments. Participants set-up the plug-and-play device themselves. For reference counts, each audio recording was counted by two annotators, and cough defined as explosive phases audio-visually labelled by both. In parallel, recordings were processed by a proprietary Albus system, comprising a deep-learning algorithm with a human screening step for verifying or excluding occasional events that mimic cough. Performance of the Albus system in detecting individual cough events and reporting hourly cough counts was compared against reference counts. Results 30 nights from 10 subjects comprised 375 hours of recording. Mean±SD coughs per night were 90±76. Coughs per hour ranged from 0 to 129. Albus counts were accurate across hours with high and low cough frequencies, with median sensitivity, specificity, positive predictive value and negative predictive values of 94.8, 100.0, 99.1 and 100.0%, respectively. Agreement between Albus and reference was strong (intra-class correlation coefficient (ICC) 0.99; 95% CI 0.99–0.99; p<0.001) and equivalent to agreement between observers and reference counts (ICC 0.98 and 0.99, respectively). Conclusions Albus Home provides a unique, contactless and accurate system for cough monitoring, enabling collection of high-quality and potentially clinically relevant longitudinal data.

AB - Introduction Objective cough frequency is a key clinical end-point but existing wearable monitors are limited to 24-h recordings. Albus Home uses contactless motion, acoustic and environmental sensors to monitor multiple metrics, including respiratory rate and cough without encroaching on patient lifestyle. The aim of this study was to evaluate measurement characteristics of nocturnal cough monitoring by Albus Home compared to manual counts. Methods Adults with respiratory conditions underwent overnight monitoring using Albus Home in their usual bedroom environments. Participants set-up the plug-and-play device themselves. For reference counts, each audio recording was counted by two annotators, and cough defined as explosive phases audio-visually labelled by both. In parallel, recordings were processed by a proprietary Albus system, comprising a deep-learning algorithm with a human screening step for verifying or excluding occasional events that mimic cough. Performance of the Albus system in detecting individual cough events and reporting hourly cough counts was compared against reference counts. Results 30 nights from 10 subjects comprised 375 hours of recording. Mean±SD coughs per night were 90±76. Coughs per hour ranged from 0 to 129. Albus counts were accurate across hours with high and low cough frequencies, with median sensitivity, specificity, positive predictive value and negative predictive values of 94.8, 100.0, 99.1 and 100.0%, respectively. Agreement between Albus and reference was strong (intra-class correlation coefficient (ICC) 0.99; 95% CI 0.99–0.99; p<0.001) and equivalent to agreement between observers and reference counts (ICC 0.98 and 0.99, respectively). Conclusions Albus Home provides a unique, contactless and accurate system for cough monitoring, enabling collection of high-quality and potentially clinically relevant longitudinal data.

UR - http://www.scopus.com/inward/record.url?scp=85143420709&partnerID=8YFLogxK

U2 - 10.1183/23120541.00265-2022

DO - 10.1183/23120541.00265-2022

M3 - Article

AN - SCOPUS:85143420709

SN - 2312-0541

VL - 8

JO - ERJ Open Research

JF - ERJ Open Research

IS - 4

M1 - 00265-2022

ER -