Supplementing the Subinternship: Effect of E-Learning Modules on Subintern Knowledge and Confidence

Anderson, Irsk; Bertram, Amanda; Stein, Alan G.; Niranjan-Azadi, Ashwini; Record, Janet D.; King, Christopher; Garber, Adam; Pahwa, Amit

doi:10.1016/j.amjmed.2021.04.012

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For both WBP and WBS, Tables 2 , 3 , 4 , 5 , 6 , and 7 present the performance metrics for SVM, LSTM, and LSTM with Bayesian optimization for each class, i.e., wheeze, crackle, and normal sounds. The above model applied in the current study is a new model applied to lung sounds (Anderson et al 2021 ), and also it is giving better results.…”

Section: Resultsmentioning

confidence: 90%

Efficient classification of the adventitious sounds of the lung through a combination of SVM-LSTM-Bayesian optimization algorithm with features based on wavelet bi-phase and bi-spectrum

Dubey

Bodade²,

Dubey

2023

Res. Biomed. Eng.

View full text Add to dashboard Cite

Background and objectives Pulmonary obstruction diseases produce adventitious sounds in the breathing cycle. With the increased impact of lung diseases, it has become essential for the medical professional to leverage artificial intelligence for faster and more accurate lung auscultation. Initial biomedical signal processing techniques focused on features based on signal amplitude, so accuracy detection depends upon the signal amplitude. The adventitious sounds heard in the respiratory cycle have non-linear characteristics. The present research targets to propose features based on the non-linearity of the adventitious sounds. Also, in this research, SVM-LSTM with the Bayesian optimization model is applied for the first time to test features of adventitious sounds. Methods The characteristics of adventitious sounds contain non-linearities. Targeting the same, the research proposes two feature sets based on wavelet bi-spectrum and bi-phase (eight each). SVM-LSTM analyzes these features with the Bayesian optimization algorithm model. The research employs the RALE database, which is the most comprehensive public database of lung sounds. Results The results are presented in a matrix of 3×10 with parameters as MSE, PSNR, R-value, RMSE, and NRMSE from the confusion matrix for SVM, SVM-LSTM, and SVM-LSTM with BO for each class, i.e., wheeze, crackle, and normal. The results are evaluated using Matlab 2021b (MathWorks , Inc.). Results reveal that feature sets achieved an accuracy of 94.086% for SVM, 94.684% for SVM-LSTM, and 95.699% with 95.161% for LSTM Bayesian optimization for WBS and WBP, respectively. Conclusion The research supports the hypothesis that adventitious sounds have non-linear properties. New features are more effective in detecting lung sounds. Also, combining the LSTM with Bayesian optimization improved each class’s accuracy and statistical parameters. The above model design achieved accurate AI-aided detection of lung diseases for light weighted edge devices.

show abstract

Section: Resultsmentioning

confidence: 90%