Unsupervised heart abnormality detection based on phonocardiogram analysis with Beta Variational Auto-Encoders
This addresses the problem of detecting cardiovascular diseases without labeled abnormal data, which is incremental as it adapts an existing method to a specific domain.
The paper tackles unsupervised detection of heart abnormalities from phonocardiogram signals using a beta variational auto-encoder, achieving an AUC of 0.91 on same-source data.
Heart Sound (also known as phonocardiogram (PCG)) analysis is a popular way that detects cardiovascular diseases (CVDs). Most PCG analysis uses supervised way, which demands both normal and abnormal samples. This paper proposes a method of unsupervised PCG analysis that uses beta variational auto-encoder ($β-\text{VAE}$) to model the normal PCG signals. The best performed model reaches an AUC (Area Under Curve) value of 0.91 in ROC (Receiver Operating Characteristic) test for PCG signals collected from the same source. Unlike majority of $β-\text{VAE}$s that are used as generative models, the best-performed $β-\text{VAE}$ has a $β$ value smaller than 1. Further experiments then find that the introduction of a light weighted KL divergence between distribution of latent space and normal distribution improves the performance of anomaly PCG detection based on anomaly scores resulted by reconstruction loss. The fact suggests that anomaly score based on reconstruction loss may be better than anomaly scores based on latent vectors of samples