Investigation Of The Correlation Between Pulmonary Pathophysiology And Recorded Chest Sound Using Simplified Ai Model

Abstract

Cardiovascular disease is a pressing problem worldwide, a majority of which can first present themselves with similar symptoms to pulmonary pathologies, complicating the early diagnosis. Unlike the modern diagnostic tools such as electrocardiogram (ECG), chest computed tomography (CCT) or X-ray; auscultation is not only risk-free but also requires much simpler equipment, all the while retaining the potential to provide just as much insight into the patients’ condition as its competitors, especially when coupled with data processing. The problem then is the lack of explainability due to the complexity and subjectivity of the audio signal. To tackle this, this thesis attempted to build an artificial intelligence model based on convolutional neural network that can predict the condition of the patient based on the lung sound recorded from their chest, and explain that result with interpretation methods, in accordance with theoretical findings about lung sound, pulmonary diseases and cardiovascular diseases. The techniques used were short-time Fourier transform (STFT) as a feature extractor, principal component analysis (PCA) to further enhance the input and Gradient-weighted Class Activation Mapping (Grad-CAM) to interpret the model. The proposed model achieved a great result, with the best model achieving 95.36% of accuracy, 96.03% of precision, 96.80% of recall, and 96.41% of F1 score, which was outperforming all of the sound-based models from other studies; and provided meaningful insight about the audio signal with the use of the simple, intuitive STFT. By interpreting the model, observation showed that signals in the range of 10-40 Hz was the most meaningful for predicting heart diseases, which closely resembled physiology findings as this is almost identical to the occurrence range for heart sound; and that the 0 Hz and 4 Hz are the two major frequencies that differentiate between patients with lung disease and healthy patients.