# Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1808.04411 · HTML: https://mlsanity.com/paper/1808.04411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals](https://mlsanity.com/paper/2604.13567/) (2026)

## Abstract (arXiv)

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 %.