Spectral features and optimal Hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. (September 2022)

• Record Type: Journal Article
• Authors: Dar, Jawad Ahmad; Srivastava, Kamal Kr.; Lone, Sajaad Ahmed
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Water cycle algorithm (WCA) -- Bark frequency cepstral coefficient (BFCC) -- Hierarchical attention network -- Deep neural network (DNN); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103905
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This research work presents a robust and effective pulmonary abnormality approach known as WCSO-based HAN. The Hanning window technique and the spectral gating-based noise reduction technique are employed for pre-processing the input respiratory signal. Extracted features are used to perform final pulmonary abnormality classification process, which is carried out using HAN classifier. Performance assessment of WCSO-based HAN is done using the performance metrics, namely TPR, TNR, and accuracy with TPR of 0.943, TNR of 0.913, and accuracy of 0.923. The developed WCSO is newly designed by the incorporation of WCA and CSO algorithm. Abstract: The most important concern in the medical field is to consider the analysis of data and perform accurate diagnosis. However, the analysis of pulmonary abnormalities may depend on the diagnostic experience and the medical skills of the physicians, and is a time-consuming practice. In order to solve such issues, an efficient Water Cycle Swarm Optimizer-based Hierarchical Attention Network (WCSO-based HAN) is developed for detecting the pulmonary abnormalities from the respiratory sound signals. However, the developed optimization technique named WCSO is devised by incorporating the Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). Here, the pre-processing is performed using the Hanning window and Spectral gating-based noise reduction method in order to remove the falsifications or noises from the signal. Thereafter, the process of feature extraction is carried out to extract the significant features, such as Bark frequency Cepstral coefficient (BFCC) and the short-term features, such as spectral flux and spectral centroid. Once the significant features are extracted, classification is performed using HAN where the training procedure of HAN is carried out using WCSO. Furthermore, the developed WCSO-based HAN obtained efficient performance using True Positive Rate (TPR), True Negative Rate (TNR) and accuracy with the values of 0.943, 0.913, and 0.923 using dataset 1, respectively. All test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results.