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Diagnosing pre-existing heart diseases early in life is important as it helpsprevent complications such as pulmonary hypertension, heart rhythm problems,blood clots, heart failure and sudden cardiac arrest. To identify suchdiseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms conveyimportant information. Therefore, effectively using these two modalities ofdata has the potential to improve the disease screening process. We evaluatethis hypothesis on a subset of the PhysioNet Challenge 2016 Dataset whichcontains simultaneously acquired PCG and ECG recordings. Our novelDual-Convolutional Neural Network based approach uses transfer learning totackle the problem of having limited amounts of simultaneous PCG and ECG datathat is publicly available, while having the potential to adapt to largerdatasets. In addition, we introduce two main evaluation frameworks namedrecord-wise and sample-wise evaluation which leads to a rich performanceevaluation for the transfer learning approach. Comparisons with methods whichused single or dual modality data show that our method can lead to betterperformance. Furthermore, our results show that individually collected ECG orPCG waveforms are able to provide transferable features which could effectivelyhelp to make use of a limited number of synchronized PCG and ECG waveforms andstill achieve significant classification performance.

A Novel Transfer Learning-Based Approach for Screening Pre-existing Heart Diseases Using Synchronized ECG Signals and Heart Sounds