Bruno Machado Rocha1, Diogo Pessoa1, Alda Marques2,3, Paulo Carvalho1, Rui Pedro Paiva1. 1. University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal. 2. Lab3R-Respiratory Research and Rehabilitation Laboratory, School of Health Sciences (ESSUA), University of Aveiro, 3810-193 Aveiro, Portugal. 3. Institute of Biomedicine (iBiMED), University of Aveiro, 3810-193 Aveiro, Portugal.
Abstract
(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.
(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.
Authors: L Mendes; I M Vogiatzis; E Perantoni; E Kaimakamis; I Chouvarda; N Maglaveras; J Henriques; P Carvalho; R P Paiva Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2016-08
Authors: Wilfried Nikolaizik; Lisa Wuensch; Monika Bauck; Volker Gross; Keywan Sohrabi; Andreas Weissflog; Olaf Hildebrandt; Ulrich Koehler; Stefanie Weber Journal: ERJ Open Res Date: 2021-11-29
Authors: Jesus Antonio Sanchez-Perez; John A Berkebile; Brandi N Nevius; Goktug C Ozmen; Christopher J Nichols; Venu G Ganti; Samer A Mabrouk; Gari D Clifford; Rishikesan Kamaleswaran; David W Wright; Omer T Inan Journal: Sensors (Basel) Date: 2022-02-02 Impact factor: 3.576