An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

Abstract

In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge. Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart Sound Database. We also employ a Representation Learning (RL) approach to generate features in an unsupervised manner using Deep Recurrent Autoencoders and use Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we utilize an SVM classifier on a high-dimensional segment-level feature extracted using various functionals on short-term acoustic features, i.e., Low-Level Descriptors (LLD). An ensemble of the three different approaches provides a relative improvement of 11.13% compared to our best single sub-system in terms of the Unweighted Average Recall (UAR) performance metric on the evaluation dataset.

arXiv comment5 pages, 5 figures, Interspeech 2018 accepted manuscript

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• How transferable are features in deep neural networks?
• auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

Cited by in corpus (5)

• Towards Domain Invariant Heart Sound Abnormality Detection using Learnable Filterbanks
• NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals
• Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments
• Heart Sound Classification Considering Additive Noise and Convolutional Distortion
• Deep Attention-based Representation Learning for Heart Sound Classification