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

Artificial Intelligence in Medicine
S Jayalakshmy, Gnanou Florence Sudha

Abstract

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 (monopho...Continue Reading

Citations

Dec 31, 2020·Sensors·Bruno Machado RochaRui Pedro Paiva
Apr 13, 2021·Journal of Ambient Intelligence and Humanized Computing·M FraiwanO Hassanin
May 21, 2021·Computer Methods and Programs in Biomedicine·Dalibor CimrJan Slegr

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