Automatic Feature Extraction for Phonocardiogram Heartbeat Anomaly Detection using WaveNet VAE

Robert-George Colț, Csongor-Huba Várady

Full text: https://drive.google.com/file/d/1T07LLFGH4UldYr4C2H0CXgD19siH4TAk/view

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Abstract

We focus on automatic feature extraction for raw audio heart-beat sounds, aimed at anomaly detection applications in healthcare. We learn features with the help of an autoencoder composed by a 1D non-
causal convolutional encoder and a WaveNet decoder trained with a modified objective based on variational inference, employing the Maximum Mean Discrepancy (MMD). Moreover we model the latent distribution
using a Gaussian chain graphical model to capture temporal correlations which characterize the encoded signals. After training the autoencoder on the reconstruction task in a unsupervised manner, we test the significance of the learned latent representations by training an SVM to predict anomalies. We evaluate the methods on a problem proposed by the PASCAL Classifying Heart Sounds Challenge and we compare with
results in the literature.

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