Abstract
The incidence of respiratory diseases is increasing rapidly due to environmental pollution affecting everyone around the world. Diagnosis from cardiopulmonary hearing has been made for hundreds of years. However, this method is influenced by noise and subjectivity by the doctor, which creates uncertainty and inconsistency in screening for lung disease. Numerous studies have tried to solve these problems by recording lung sounds digitally and processing them. In this article, we use a discrete Wavelet transformation to classify wheeze, crackle and normal lung sounds to reduce computation time and cost. The dataset is taken from a published database initiated by the Internal Biomedical Health Informatics Conference (ICBHI). As characteristics and machine learning models are used to learn between respiratory traits, reconstructed sub-band energies are extracted. The feasibility and efficiency of our proposed approach have been verified by our findings.
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Tran, V. et al. (2021). Lung Sounds Classification Using Wavelet Reconstructed Sub-bands Signal and Machine Learning. In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-2094-2_27
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DOI: https://doi.org/10.1007/978-981-16-2094-2_27
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