Short-segment heart sound classification using an ensemble of deep convolutional neural networks

TL;DR

This paper introduces a deep CNN ensemble framework for classifying short heart sound segments, leveraging raw signals and time-frequency features to outperform traditional methods on a large dataset.

Contribution

It develops a novel ensemble of 1D and 2D CNNs that directly learn from raw signals and MFCC features for improved heart sound classification.

Findings

Achieved 89.22% accuracy with the ensemble model.

Outperformed traditional classifiers like SVM and HMM.

Demonstrated high sensitivity and specificity on the PhysioNet dataset.

Abstract

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time- and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.