UNSUPERVISED HEART ABNORMALITY DETECTION BASED ON PHONOCARDIOGRAM ANALYSIS WITH BETA VARIATIONAL AUTO-ENCODERS

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 (beta - 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 beta - VAEs that are used as generative models, the best-performed beta - VAE has a beta value smaller than 1. This fact demonstrates that the resampling process helps the improvements on anomaly PCG detection through reconstruction loss worth a heavier weight. Further investigations suggest that anomaly score based on reconstruction loss may be better than anomaly scores based on latent vectors of samples in PCG analysis based on VAE systems.

Automated summary

This paper proposes an unsupervised PCG analysis method using beta-VAE to model normal PCG signals, achieving a good performance. The study suggests that anomaly scores based on reconstruction loss may be better than those based on latent vectors.