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Chronic obstructive pulmonary disease is a widespread, evitable and remediable ailment accentuated by the deregulation of a stream of air in the lungs or due to pleura anomalies owing to harmful fuels. Timely detection and prevention are of utmost importance to curb the spread of these disorders. To diagnose the respiratory illness, clinicians use the traditional approach of auscultation and this led to the development of state-of-the-art technology tools for sensing the morbidities. In this pursuit, in this work, a novel deep-learning structure is framed for better classification of lung sounds with the amalgamation of features extracted using the Empirical mode decomposition technique and improved network models. In this paper, a two-stage approach is proposed to classify the acoustic files from the ICBHI benchmark dataset. At the first stage, intrinsic mode function (IMF) feature vectors are extracted from lung sounds and the best combination of IMF features to classify respiratory disorders is found. In the next stage, Gammatone filters are applied on the best combination IMF features and Gammatone cepstral coefficients (GTCC) are computed. The GTCC are input to the deep-learning model, Recurrent Neural Network-based stacked BiLSTM classifier for classification. It is observed that the IMF 3 has more meaningful information and enhances the performance in conjunction with GTCCs compared to other IMFs and MFCC. Moreover, the results demonstrate that the proposed GTCC of the third IMF component applied to the stacked BiLSTM framework excels the competing Convolutional Neural Network method of classification in terms of accuracy, specificity and sensitivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]