Automatic Feature Extraction for Heartbeat Anomaly Detection
- URL: http://arxiv.org/abs/2102.12289v1
- Date: Wed, 24 Feb 2021 13:55:24 GMT
- Title: Automatic Feature Extraction for Heartbeat Anomaly Detection
- Authors: Robert-George Colt and Csongor-Huba V\'arady and Riccardo Volpi and
Luigi Malag\`o
- Abstract summary: 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.
- Score: 7.054093620465401
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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