Codec Data Augmentation for Time-domain Heart Sound Classification

TL;DR

This paper introduces a codec data augmentation technique for time-domain heart sound classification, significantly improving model accuracy by addressing limited training data issues.

Contribution

It proposes a novel codec simulation data augmentation method that enhances heart sound classification performance beyond existing models.

Findings

Data augmentation reduces classification error from 0.8 to 0.2.

Codec simulation outperforms baseline models.

Augmentation effectively mitigates data scarcity.

Abstract

Heart auscultations are a low-cost and effective way of detecting valvular heart diseases early, which can save lives. Nevertheless, it has been difficult to scale this screening method since the effectiveness of auscultations is dependent on the skill of doctors. As such, there has been increasing research interest in the automatic classification of heart sounds using deep learning algorithms. However, it is currently difficult to develop good heart sound classification models due to the limited data available for training. In this work, we propose a simple time domain approach, to the heart sound classification problem with a base classification error rate of 0.8 and show that augmentation of the data through codec simulation can improve the classification error rate to 0.2. With data augmentation, our approach outperforms the existing time-domain CNN-BiLSTM baseline model. Critically, our experiments show that codec data augmentation is effective in getting around the data limitation.