Title: Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks,
Abstract: In this article, we propose a novel technique for classification of the
Murmurs in heart sound. We introduce a novel deep neural network architecture
using parallel combination of the Recurrent Neural Network (RNN) based
Bidirectional Long Short-Term Memory (BiLSTM) & Convolutional Neural Network
(CNN) to learn visual and time-dependent characteristics of Murmur in PCG
waveform. Set of acoustic features are presented to our proposed deep neural
network to discriminate between Normal and Murmur class. The proposed method
was evaluated on a large dataset using 5-fold cross-validation, resulting in a
sensitivity and specificity of 96 +- 0.6 % , 100 +- 0 % respectively and F1
Score of 98 +- 0.3 %.