  Cardiac auscultation is the most practiced non-invasive and cost-effective
procedure for the early diagnosis of heart diseases. While machine learning
based systems can aid in automatically screening patients, the robustness of
these systems is affected by numerous factors including the stethoscope/sensor,
environment, and data collection protocol. This paper studies the adverse
effect of domain variability on heart sound abnormality detection and develops
strategies to address this problem. Methods: We propose a novel Convolutional
Neural Network (CNN) layer, consisting of time-convolutional (tConv) units,
that emulate Finite Impulse Response (FIR) filters. The filter coefficients can
be updated via backpropagation and be stacked in the front-end of the network
as a learnable filterbank. Results: On publicly available multi-domain
datasets, the proposed method surpasses the top-scoring systems found in the
literature for heart sound abnormality detection (a binary classification
task). We utilized sensitivity, specificity, F-1 score and Macc (average of
sensitivity and specificity) as performance metrics. Our systems achieved
relative improvements of up to 11.84% in terms of MAcc, compared to
state-of-the-art methods. Conclusion: The results demonstrate the effectiveness
of the proposed learnable filterbank CNN architecture in achieving robustness
towards sensor/domain variability in PCG signals. Significance: The proposed
methods pave the way for deploying automated cardiac screening systems in
diversified and underserved communities.
