Source: International Congress 2016 – Monitoring airway diseases with clinical tools
Disease area: Airway diseases

Abstract

The monitoring of productive and non-productive cough is important to proof the effect of therapeutic approaches such as antitussive medicine or vibrating vests.Currently, this monitoring only consists of questionnaires or video and audio observations perfomed by experts. Questionnaires are showing the impact on quality of life. They neither count coughing events, nor classify them. Furthermore, manual rating by video and audio data is very time consuming.We used the LEOSound Monitor (Heinen und Löwenstein, Germany), a lung sound recorder including a validated detection algorithm for coughing and wheezing events. Our aim was to develop an algorithm to automatically classify these events in productive and non-productive cough.By now, we analyzed eleven patients (4 f, 7 m, Ø69.8 ± 11.8 years) with different respiratory disease such as chronic obstructive pulmonary disease and asthma. The recorded sound data was 8 to 12 hours each.All coughing events were rated by pulmonary medicine experts and also by the developed classification algorithm. Results indicate a high correlation between experts and algorithm with sensitivity and specificity above 90 %.We could show that an automated classification of productive and non-productive cough is possible in principle. Further studies are needed to evaluate the clinical use.


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Citations should be made in the following way:
Volker Gross (Giessen, Germany), Volker Gross, Patrick Fischer, Nicole Kerkenberg, Christoph Urban, Andreas Weissflog, Ulrich Koehler, Keywan Sohrabi. Automated classification of productive and non-productive cough. Eur Respir J 2016; 48: Suppl. 60, 1045

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