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Sensors 2015, 15(10), 26978-26996; doi:10.3390/s151026978

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

1
Biomedical Engineering and Telemedicine Research Group, University of Cadiz. Avda. de la Universidad, 10, 11519 Puerto Real, Cadiz, Spain
2
Department of Automation, Electronics and Computer Architecture and Networks, University of Cadiz. Avda. de la Universidad, 10, 11519 Puerto Real, Cadiz, Spain
3
Pulmonology, Allergy and Thoracic Surgery Unit, Puerta del Mar University Hospital, 11009 Cadiz, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Alexander Star
Received: 14 August 2015 / Revised: 30 September 2015 / Accepted: 19 October 2015 / Published: 23 October 2015
(This article belongs to the Section Biosensors)
View Full-Text   |   Download PDF [4156 KB, uploaded 23 October 2015]   |  

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. View Full-Text
Keywords: chronic obstructive pulmonary disease; COPD; data-driven; early detection; exacerbation; lung sounds; machine learning; PCA; prediction; respiratory sounds; sensor; support vector machine; SVM; symptoms; telehealth; telemonitoring chronic obstructive pulmonary disease; COPD; data-driven; early detection; exacerbation; lung sounds; machine learning; PCA; prediction; respiratory sounds; sensor; support vector machine; SVM; symptoms; telehealth; telemonitoring
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fernandez-Granero, M.A.; Sanchez-Morillo, D.; Leon-Jimenez, A. Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD. Sensors 2015, 15, 26978-26996.

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