engleski

Classification Accuracy Comparison of Asthmatic Wheezing Sounds Recorded under Ideal and Real- world Conditions

Asthma is the most common chronic disease among children. Diagnosis of asthma is often challenging, so the computerized lung sound analysis is important diagnostic aid. This research compares the efficiency of the classification algorithms applied both on signals available on the internet and signals recorded on children in real-life clinical settings. With an appropriate signal processing technique, resulting in MFCC features, it is possible to achieve high classification accuracy for signals recorded in suboptimal conditions.

machine learning; classification; asthma; phonopneumogram; MFCC; SVM; k-NN

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