Classifying respiratory sounds with different feature sets

Area of Science:

• Medical Signal Processing
• Biomedical Engineering
• Computational Health

Background:

• Respiratory sound analysis is crucial for diagnosing lung conditions.
• Automated classification systems can aid clinicians in interpreting complex respiratory signals.
• Previous studies have explored various machine learning approaches for respiratory sound classification.

Purpose of the Study:

• To evaluate the performance of k-nearest neighbors (k-NN) and artificial neural network (ANN) classifiers for respiratory sound classification.
• To compare the effectiveness of different feature sets, including AR model coefficients, wavelet coefficients, and crackle parameters, in classifying respiratory sounds.
• To differentiate between healthy and pathological respiratory sound signals using machine learning models.

Main Methods:

• Respiratory sound signals were acquired using a single microphone on the posterior chest.
• Each respiration cycle was divided into sixty segments for feature extraction.
• Feature sets included 6th order AR model coefficients, wavelet coefficients, and crackle parameters.
• k-NN and ANN classifiers were applied to classify inspiration and expiration phases separately.

Main Results:

• Both k-NN and ANN classifiers demonstrated capability in classifying respiratory sound signals.
• The performance varied depending on the feature set used, indicating the importance of feature selection.
• Successful differentiation between healthy and pathological respiratory sound classes was achieved.

Conclusions:

• k-NN and ANN classifiers are viable tools for automated respiratory sound analysis.
• The choice of feature set significantly impacts classification accuracy.
• These computational methods hold promise for improving the diagnosis of respiratory diseases.