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According to the World Health Organization, Chronic Obstructive Pulmonary Disease is one of the leading causes of death in countries across all income groups. It is a noncommunicable, respiratory disease characterized by persistent reduction of airflow to and from the lungs. A leading cause of COPD is smoking tobacco (active and passive), raising air pollution, and occupational dust and chemicals. Emphysema and chronic bronchitis are the most common conditions that make up COPD. The damage to the lungs from COPD cannot be cured but treatment can relieve symptoms, improve quality of life, and reduce the risk of death. Early diagnosis is an important area of research in recent years. A detailed Pulmonary Function Test and Spirometry can help in the clinical diagnosis of the condition but requires the patient to exhale with force making it inaccessible for heart patients and other conditions. Lung sounds are an excellent way of observing the condition and performing diagnosis that is more patient accessible and friendly. Lung sounds for COPD patients have characteristics of wheeze, Rhonchi, and crackle sounds. To diagnose this condition Mel Frequency Cepstral Coefficients (MFCC) of Lung sounds are extracted and a Support Vector Machine (SVM) based diagnostic model is developed. Experiments were conducted to optimize the performance of SVM by analyzing two parameters during feature extraction, the significance of the Energy Parameter, and by varying the window length and results discussed. A base model was developed using the default settings of MFCC as discussed in the literature. The analysis was done to identify the optimal feature parameters to best diagnose lung sounds. The proposed model was successful in achieving high classification performance accuracy of 92.5% from a base model of 75.9% demonstrating a performance improvement of 16.6%. Keywords— COPD, MFCC, SVM, wheeze, Rhonchi, and crackle

Diagnosis of Chronic Obstructive Pulmonary Disease from Lung Sounds using Support Vector Machine