BackgroundPulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database.ResultsThe pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively.ConclusionAlthough the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.
Bariatric and metabolic surgery (BMS), the only effective option for patients with obesity with or without comorbidities, has been stopped temporarily due to the ongoing novel corona virus disease (COVID-19) pandemic. However, there has been a recent change in the governmental strategy of dealing with this virus from 'Stay at Home' to 'Stay Alert' in many countries including India. A host of health services including elective surgeries are being resumed. In view of the possibility of resumption of BMS in near future, Obesity and Metabolic Surgery Society of India (OSSI) constituted a committee of experienced surgeons to give recommendations about the requirements as well as precautions to be taken to restart BMS with emphasis on safe delivery and high-quality care.
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