Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?

Area of Science:

• Medical Technology
• Artificial Intelligence in Healthcare
• Respiratory Medicine

Background:

• Adventitious respiratory sounds (ARS), like wheezes and crackles, are common in respiratory conditions.
• The duration of ARS events can vary significantly.
• Current automatic classification methods may not fully account for variable event durations.

Purpose of the Study:

• To investigate the influence of adventitious respiratory sound event duration on automatic classification performance.
• To assess how the inclusion of an 'other' class (negative class) impacts classifier accuracy.
• To evaluate the performance of different machine learning algorithms under varying experimental conditions.

Main Methods:

• Experiments were designed to vary the durations of 'other' events in classification tasks.
• Three tasks were evaluated: crackle vs. wheeze vs. other (3 Class), crackle vs. other (2 Class Crackles), and wheeze vs. other (2 Class Wheezes).
• Four classifiers, including linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks, were tested on an open-access respiratory sound database.

Main Results:

• The best-performing classifier achieved 96.9% accuracy on a 3 Class task with fixed event durations.
• Under more realistic conditions with variable durations, the same classifier's accuracy dropped to 81.8% on the 3 Class task.
• Classifier performance decreased substantially when evaluated with variable event durations compared to fixed durations.

Conclusions:

• The experimental design significantly impacts the assessment of automatic ARS classification algorithms.
• Automatic classification of ARS is not a solved problem, as performance degrades under complex, realistic evaluation scenarios.
• Further research is needed to develop robust ARS classification algorithms that account for variable event durations.