Long Short Term Memory Based Recurrent Neural Network for Wheezing Detection in Pulmonary Sounds

This paper presents a new technique for wheezes detection in respiratory sounds using a Long Short-Term Memory (LSTM), a specific type of Recurrent Neural Networks (RNNs). The purpose of this work is to develop an LSTM-based system and compare its classification performances to those obtained by a Multilayer Perceptron (MLP) feed-forward network. The MLP is a widely used neural network that has proven its efficiency in respiratory sound classification. Feed-forward networks do not consider time dependencies, while RNNs reach their limit in detecting dependencies when they occur at long time intervals. Because wheezing occurs over several consecutive intervals, we assume that LSTM takes into account the changing characteristics better than MLP and provides better results. Pulmonary sounds are characterized using the Mel-Frequency Cepstral Coefficients (MFCC) method before applying the LSTM-based classifier. As expected, the experimental tests show that LSTM takes advantage of the long-term dependencies observed in wheezing sounds to lead to better classification performances (accuracy of 91%).