Unsupervised heart abnormality detection based on phonocardiogram analysis with beta variational auto-encoders

Shengchen Li, Ke Tian, Rui Wang

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

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 − 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 − VAEs that are used as generative models, the best-performed β − VAE has a β value smaller than 1. This fact demonstrates that the re-sampling 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.

Original languageEnglish
Pages (from-to)8353-8357
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Anomaly Detection
  • Outlier Detection
  • Phonocardiogram Analysis
  • Unsupervised Learning
  • Variational-Auto-Encoder

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