Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks

Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. In this paper, a pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders. The proposed approach models the segmented respiratory sound signal into Bump and Morse scalograms from several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) method. From the extracted intrinsic mode functions, the percentage energy calculated for each wavelet coefficient in the form of scalograms are computed. Subsequently, these scalograms are given as input to the pre-trained optimized CNN model for training and testing. Stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms were examined to check the prediction accuracy on the dataset comprising of four classes of lung sounds, normal, crackles (coarse and fine), wheezes (monophonic & polyphonic) and low-pitched wheezes (Rhonchi). On comparison to the baseline method of standard Bump and Morse wavelet transform approach which produced 79.04 % and 81.27 % validation accuracy, an improved accuracy of 83.78 % is achieved by the virtue of scalogram representation of various IMFs of EMD. Hence, the proposed approach achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature.