Recognizing Abnormal Heart Sounds Using Deep Learning
This work addresses the problem of cost-sensitive misclassification in heart sound analysis for medical diagnostics, though it is incremental as it builds on existing deep learning methods with a modified loss function.
The paper tackled automated cardiac auscultation by developing a deep learning algorithm to recognize abnormal heart sounds, achieving a specificity of 0.95, sensitivity of 0.73, and an overall score of 0.84 in a challenge, with the highest specificity among entries.
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.