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Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Fernández-Granero, M.A.; ; | Sánchez-Morillo, D. | León-Jiménez, A. | Crespo, L.F.

Affiliations: Biomedical Engineering and Telemedicine Research Group, University of Cádiz, Cádiz, Spain | Pulmonology and Allergy Unit, Puerta del Mar University Hospital, Cádiz, Spain

Note: [] The work presented here was carried out in collaboration between all authors: they all defined the research theme, read abstracts, selected reviews for full text assessment, reviewed related papers, designed, conducted the study, interpreted its results, revised the manuscript and approved the final version. MAFG and DSM managed the review process. MAFG and DSM drafted the full article and revised it critically for intellectual content. MAFG and DSM carried out the computerized processing of data and ALJ gave expert respiratory input and ALJ and LFC contributed to study conceptualization and refining on outcome measures.

Note: [] Corresponding author: M.A. Fernández-Granero, Biomedical Engineering and Telemedicine Research Group, University of Cádiz, Cádiz, Spain. Tel.: + 0034956015155; E-mail: [email protected].

Keywords: Chronic Obstructive Pulmonary Disease, exacerbation, telehealth, symptoms, questionnaire, early detection, Probabilistic Neural Network, telemonitoring

DOI: 10.3233/BME-141212

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3825-3832, 2014

Published: 2014
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Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8±1.8 days (average ± SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.

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