  Objective: Heart murmurs are abnormal sounds caused by turbulent blood flow
within the heart. Several diagnostic methods are available to detect heart
murmurs and their severity, such as cardiac auscultation, echocardiography,
phonocardiogram (PCG), etc. However, these methods have limitations, including
extensive training and experience among healthcare providers, cost and
accessibility of echocardiography, as well as noise interference and PCG data
processing. This study aims to develop a novel end-to-end real-time heart
murmur detection approach using traditional and depthwise separable
convolutional networks. Methods: Continuous wavelet transform (CWT) was applied
to extract meaningful features from the PCG data. The proposed network has
three parts: the Squeeze net, the Bottleneck, and the Expansion net. The
Squeeze net generates a compressed data representation, whereas the Bottleneck
layer reduces computational complexity using a depthwise-separable
convolutional network. The Expansion net is responsible for up-sampling the
compressed data to a higher dimension, capturing tiny details of the
representative data. Results: For evaluation, we used four publicly available
datasets and achieved state-of-the-art performance in all datasets.
Furthermore, we tested our proposed network on two resource-constrained
devices: a Raspberry PI and an Android device, stripping it down into a tiny
machine learning model (TinyML), achieving a maximum of 99.70%. Conclusion: The
proposed model offers a deep learning framework for real-time accurate heart
murmur detection within limited resources. Significance: It will significantly
result in more accessible and practical medical services and reduced diagnosis
time to assist medical professionals. The code is publicly available at TBA.
