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 ($\beta-\text{VAE}$) 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 $\beta-\text{VAE}$s that are used as generative models, the best-performed $\beta-\text{VAE}$ has a $\beta$ 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

中文翻译：

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基于心电图分析和Beta变分自动编码器的无监督心脏异常检测

心音（也称为心电图（PCG））分析是检测心血管疾病（CVD）的一种流行方法。大多数PCG分析使用监督方式，这需要正常和异常样品。本文提出了一种无监督的PCG分析方法，该方法使用Beta变分自动编码器（$ \ beta- \ text {VAE} $）对正常PCG信号进行建模。对于从同一来源收集的PCG信号，在ROC（接收机工作特性）测试中，性能最佳的模型达到的AUC（曲线下面积）值为0.91。与大多数用作生成模型的$ \ beta- \ text {VAE} $ s不同，性能最佳的$ \ beta- \ text {VAE} $的$ \ beta $值小于1。然后，进一步的实验发现，在潜在空间分布与正态分布之间引入轻量级KL散度可改善基于重建损失导致的异常评分的异常PCG检测的性能。事实表明，基于重建损失的异常评分可能比基于样本潜在矢量的异常评分更好。