Recognizing Abnormal Heart Sounds Using Deep Learning

TL;DR

This paper presents a deep learning approach using CNNs and time-frequency heat maps to automatically classify normal and abnormal heart sounds, achieving high specificity and competitive overall accuracy.

Contribution

It introduces a novel CNN-based algorithm with a cost-sensitive loss function for improved heart sound abnormality detection.

Findings

Achieved 0.95 specificity in classifying abnormal heart sounds.

Attained 0.73 sensitivity, demonstrating effective abnormal sound detection.

Outperformed all challenge entries in specificity, with a near-top overall score.

Abstract

The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.