![]() One of the leading causes of death worldwide is respiratory disorder diseases (WHO 2019). The above model design achieved accurate AI-aided detection of lung diseases for light weighted edge devices. Also, combining the LSTM with Bayesian optimization improved each class’s accuracy and statistical parameters. New features are more effective in detecting lung sounds. The research supports the hypothesis that adventitious sounds have non-linear properties. 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. The research employs the RALE \(^\), Inc.). SVM-LSTM analyzes these features with the Bayesian optimization algorithm model. Targeting the same, the research proposes two feature sets based on wavelet bi-spectrum and bi-phase (eight each). The characteristics of adventitious sounds contain non-linearities. Also, in this research, SVM-LSTM with the Bayesian optimization model is applied for the first time to test features of adventitious sounds. The present research targets to propose features based on the non-linearity of the adventitious sounds. The adventitious sounds heard in the respiratory cycle have non-linear characteristics. Initial biomedical signal processing techniques focused on features based on signal amplitude, so accuracy detection depends upon the signal amplitude. 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. Wolters Kluwer Health: Philadelphia.Pulmonary obstruction diseases produce adventitious sounds in the breathing cycle. Bate’s Guide to Physical Examination and History Taking (13 th ed.). Such conditions include atelectasis, severe COPD, severe asthma, pneumothorax, tension pneumothorax, and extrinsic bronchial compression from tumor.īickley, L.
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