Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression

• Main Authors: Sean Andrei, P. Co, Madamba, Claudine Anne J., Guico, Ma. Leonora, Galicia, Jan Kevin A
• Format: text
• Published: Archīum Ateneo 2023
• Subjects: cough classification; logistic regression; support vector machine; wet and dry cough; Biomedical; Electrical and Computer Engineering; Engineering
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/147; https://doi.org/10.1109/ICCCE58854.2023.10246105
• Institution: Ateneo De Manila University

Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to extract and analyze the Energy Envelope Peaks, Crest Factors, Zero-Crossings, and Formant Frequencies 1-4 from a dataset of 870 cough samples. The analysis of 347 wet cough sound samples and 523 dry cough samples reveals distinctive characteristics. Wet coughs exhibit a higher number of peaks and zero-crossings, while dry coughs display a slightly higher crest factor on average. Moreover, the F1 and F2 formant frequencies are higher in wet coughs, whereas the F3 and F4 formant frequencies are higher in dry coughs. To classify the cough types, both Support Vector Machine (SVM) and Logistic Regression Method (LRM) classifiers are trained using the identified features. The SVM classifier achieves an average accuracy of 71.26%, sensitivity of 72.73%, specificity of 70.87%, and F1-score of 67.94% during testing. Similarly, the LRM classifier achieves an accuracy of 71.26%, sensitivity of 70.59%, specificity of 71.55%, and F1-score of 68.45%. Such automated classification systems have the potential to aid in the early detection and monitoring of respiratory diseases in enclosed spaces.