HeartSiam: A Domain Invariant Model for Heart Sound Classification

TL;DR

This paper introduces HeartSiam, a Siamese network model that achieves domain-invariant heart sound classification across different stethoscope recordings, outperforming previous methods in accuracy and specificity.

Contribution

The novel contribution is the development of a Siamese network architecture that learns similarity and difference across domains for robust heart sound classification.

Findings

Achieved 82.8% sensitivity and 75.3% specificity.

Surpassed the challenge's top method in specificity and accuracy.

Converges faster and performs better than similar state-of-the-art models.

Abstract

Cardiovascular disease is one of the leading causes of death according to WHO. Phonocardiography (PCG) is a costeffective, non-invasive method suitable for heart monitoring. The main aim of this work is to classify heart sounds into normal/abnormal categories. Heart sounds are recorded using different stethoscopes, thus varying in the domain. Based on recent studies, this variability can affect heart sound classification. This work presents a Siamese network architecture for learning the similarity between normal vs. normal or abnormal vs. abnormal signals and the difference between normal vs. abnormal signals. By applying this similarity and difference learning across all domains, the task of domain invariant heart sound classification can be well achieved. We have used the multi-domain 2016 Physionet/CinC challenge dataset for the evaluation method. Results: On the evaluation set provided by the challenge, we have achieved a sensitivity of 82.8%, specificity of 75.3%, and mean accuracy of 79.1%. While overcoming the multi-domain problem, the proposed method has surpassed the first-place method of the Physionet challenge in terms of specificity up to 10.9% and mean accuracy up to 5.6%. Also, compared with similar state-of-the-art domain invariant methods, our model converges faster and performs better in specificity (4.1%) and mean accuracy (1.5%) with an equal number of epochs learned.