Abstract
Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients’ quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.
Highlights
Chronic obstructive pulmonary disease (COPD) is a primary cause of chronic morbidity and ranked as the third commonest cause of death in the world between 1990 and 2010 [1]
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are important episodes in the course of the disease associated with a significant increase in mortality, hospitalisation and health-care use and impaired quality of life
In the absence of biomarkers or consolidated markers to early detect AECOPD, this study presents an exploratory study of the feasibility of using a respiratory sensor embedded in a special housing for self-use of ageing users, computerised analyses of respiratory sounds (CARS) and data-mining techniques for the remote early detection of the AECOPD according to the settings described in [23]
Summary
Chronic obstructive pulmonary disease (COPD) is a primary cause of chronic morbidity and ranked as the third commonest cause of death in the world between 1990 and 2010 [1]. It has aroused a growing research interest as a major public health concern because of its mortality, prevalence and the resulting increased use of healthcare resources. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are important episodes in the course of the disease associated with a significant increase in mortality, hospitalisation and health-care use and impaired quality of life. Exacerbations are defined as acute events, characterised by a worsening of the patient's respiratory symptoms from the stable state and beyond day-to-day variation, leading to a change in medical treatment and/or hospitalisation [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
