Detection of Wheeze Sounds in Respiratory Disorders: A Deep Learning Approach

Abstract

Respiratory disorders, including chronic obstructive pulmonary disease (COPD) and asthma, are major causes of death globally. Early diagnosis of these conditions is essential for effective treatment. Auscultation of the lungs is the traditional diagnostic method, which has drawbacks such as subjectivity and susceptibility to environmental interference. To overcome these limitations, this study presents a novel approach for wheeze detection using deep learning methods. This approach includes the usage of artificial data created by employing the open ICBHI dataset with the aim of improvement in generalization of learning models. Spectrograms that were obtained as the output of the Short-Time Fourier Transform analysis were employed in feature extraction. Two labeling approaches were used for model comparison. The first approach involved labeling after wheezing occurred, and the second approach assigned labels directly to the time steps where wheezing patterns are seen. Wheeze event detection was performed by constructing four RNN-based models (CNN-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU). It was observed that labeling wheeze events directly resulted in more precise detection, with exceptional performance exhibited by the CNN-BiLSTM model. This approach demonstrates the potential for improving respiratory disorders diagnosis and hence leading to improved patient care.

Keywords

• Recurrent Neural Networks
• Long Short-Term Memory
• Short Time Fourier Transform
• Wheezes
• Respiratory Sounds

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