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arXiv · 2102.12289

Automatic Feature Extraction for Heartbeat Anomaly Detection

Robert-George Colt·Csongor-Huba Várady·Riccardo Volpi·Luigi Malagò
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Abstract

We focus on automatic feature extraction for raw audio heartbeat 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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Robert-George Colt, Csongor-Huba Várady, Riccardo Volpi, Luigi Malagò. 2021-02-24. Automatic Feature Extraction for Heartbeat Anomaly Detection. https://arxiv.org/abs/2102.12289

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