Nighttime continuous contactless smartphone-based cough monitoring for the ward: A validation study.

BACKGROUND: Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, non-invasive ward monitoring and allow the detection of subtle changes in vital signs. Cough holds great potential for monitoring through mobile technologies on the ward as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward.

OBJECTIVE: This exploratory study involves the validation of an automated smartphone-based monitoring system for continuous cough counting in two different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real-time.

METHODS: Automated cough counts are measured consistently on-device and on-computer, and compared with cough and non-cough sounds counted manually over eight hours long nocturnal recordings in nine patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds, and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble Convolutional Neural Network developed and trained on asthmatic cough data.

RESULTS: In this validation study, a total of 72 hours of recording from nine participants with pneumonia, four of whom were infected with SARS-CoV-2, were analyzed. All recordings were subjected to manual analysis by two blinded raters. The proposed system yielded sensitivity and specificity of 72% and 99% on-device and 82% and 99% on-computer, respectively, for detecting coughs. The mean difference between the automated and human rater cough counts were -1.0, CI 95% [-12.3, 10.2] and -0.9, CI 95% [-6.5, 4.8] coughs per hour within-subject for the on-device and on-computer mode, respectively.

CONCLUSIONS: The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.