Rep.James Grotberg, professor of biomedical engineering at the College of Engineering and professor of surgery at the Medical School, recently published a study describing how the mechanics that produce those noises with every breath are likely a cause of injury and inflammation. Kim, Y., Hyon, Y., Jung, S.S., Lee, S., Yoo, G., Chung, C., Ha, T.: Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Hsu, F.S., Huang, S.R., Huang, C.W., Huang, C.J., Cheng, Y.R., Chen, C.C., Chen, Y.T., Lai, F.: Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF Lung V1. Lu, X., Bahoura, M.: An integrated automated system for crackles extraction and classification. Jin, F., Sattar, F., Goh, D.Y.: New approaches for spectro-temporal feature extraction with applications to respiratory sound classification. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. A., Charleston-Villalobos, S., Gonzalez-Camarena, R., Aljama-Corrales, T.: Analysis of discontinuous adventitious lung sounds by Hilbert-Huang spectrum. Rocha, B.M., Pessoa, D., Marques, A., Carvalho, P., Paiva, R.P.: Automatic classification of adventitious respiratory sounds: a (un) solved problem? Sensors 21(1), 57 (2020) 23(3), 1012–1021 (2013)Īsatani, N., Kamiya, T., Mabu, S., Kido, S.: Classification of respiratory sounds using improved convolutional recurrent neural network. Serbes, G., Sakar, C.O., Kahya, Y.P., Aydin, N.: Pulmonary crackle detection using time-frequency and time-scale analysis. Ponte, D.F., Moraes, R., Hizume, D.C., Alencar, A.M.: Characterization of crackles from patients with fibrosis, heart failure and pneumonia. Sarkar, M., Madabhavi, I., Niranjan, N., Dogra, M.: Auscultation of the respiratory system. Jones, A., Jones, R.D., Kwong, K., Burns, Y.: Effect of positioning on recorded lung sound intensities in subjects without pulmonary dysfunction. Pramono, R.X.A., Bowyer, S., Rodriguez-Villegas, E.: Automatic adventitious respiratory sound analysis: a systematic review. Villanueva, C., Vincent, J., Slowinski, A., Hosseini, M.P.: Respiratory sound classification using long short term memory. Shi, L., Du, K., Zhang, C., Ma, H., Yan, W.: Lung sound recognition algorithm based on vggish-bigru. In: Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. Nguyen, T., & Pernkopf, F.: Lung sound classification using snapshot ensemble of convolutional neural networks. Sengupta, N., Sahidullah, M., Saha, G.: Lung sound classification using cepstral-based statistical features. Jácome, C., Aviles-Solis, J.C., Uhre, Å.M., Pasterkamp, H., Melbye, H.: Adventitious and normal lung sounds in the general population: comparison of standardized and spontaneous breathing. Moorthy, D.P., Harikrishna, M., Mathew, J., Sathish, N.: Sound classification for respiratory diseases using machine learning technique.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |