An efficient lung sound classification technique based on MFCC and HDMR
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02672-2.pdf
Reference38 articles.
1. Fraiwan, M., Fraiwan, L., Alkhodari, M., Hassanin, O.: Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory. J. Ambient. Intell. Humaniz. Comput. 13, 4759–4771 (2021). https://doi.org/10.1007/s12652-021-03184-y
2. Petmezas, G., et al.: Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function. Sensors (2022). https://doi.org/10.3390/s22031232
3. Kim, Y., et al.: Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci. Rep. 11(1), 1–11 (2021). https://doi.org/10.1038/s41598-021-96724-7
4. Sarkar, M., Madabhavi, I., Niranjan, N., Dogra, M.: Auscultation of the respiratory system. Ann. Thorac. Med. 10(3), 158 (2015). https://doi.org/10.4103/1817-1737.160831
5. Palaniappan, R., Sundaraj, K., Sundaraj, S.: Artificial intelligence techniques used in respiratory sound analysis—a systematic review. Biomedizinische Technik/Biomed. Eng. 59(1), 7–18 (2014). https://doi.org/10.1515/bmt-2013-0074
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