Journal
SENSORS
Volume 21, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/s21010057
Keywords
adventitious respiratory sounds; experimental design; machine learning
Funding
- Fundacao para a Ciencia e Tecnologia (FCT) [SFRH/BD/135686/2018]
- Horizon 2020 Framework Programme of the European Union [825572]
- Fundo Europeu de Desenvolvimento Regional (FEDER) through Programa Operacional Competitividade e Internacionalizacao (COMPETE)
- FCT [UID/BIM/04501/2013, POCI-01-0145-FEDER-007628-iBiMED]
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The study showed a significant impact of event duration on automatic ARS classification, highlighting the importance of experimental design in assessing algorithm performance. Additionally, the automatic classification of ARS is not yet a solved problem, as algorithms' performance substantially decreases under complex evaluation scenarios.
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers' performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms' performance decreases substantially under complex evaluation scenarios.
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