Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks
This work addresses heart murmur detection for medical diagnosis, presenting an incremental improvement with a hybrid method.
The paper tackled the problem of detecting murmurs in heart sounds by proposing a novel deep neural network architecture combining parallel RNN and CNN, achieving a sensitivity of 96 ± 0.6%, specificity of 100 ± 0%, and F1 score of 98 ± 0.3% on a large dataset.
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 %.