Automatic Feature Extraction for Heartbeat Anomaly Detection
This work addresses anomaly detection in healthcare for potential diagnostic applications, but it is incremental as it builds on existing autoencoder and benchmark methods.
The paper tackles the problem of detecting anomalies in heartbeat sounds by automatically extracting features using a novel autoencoder architecture with a WaveNet decoder and Gaussian chain latent model, achieving competitive performance on the PASCAL Classifying Heart Sounds Challenge benchmark.
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.