Time and Frequency Domain Performance Comparison for Wheeze Detection Using K-Nearest Neighbor

Abstract:

In this Paper, the Comparison between the Performance of Wheezes Data Processing in the Frequency Domain and in the Time Domain is Evaluated Using K-Nearest Neighbor (KNN). the Purpose of this Paper is to Clarify the Confusion Regarding the Methods Used Nowadays, as many of the Previous Researchers have Stated that Wheezes Data are Better Processed in the Frequency Domain due to its Dominant Frequency Peaks but Not a Single Researcher has Made a Direct Comparison to Prove the Reliability of the Method Used. from the Evaluation Made, the Result Shows that the Performance of Wheeze Data Processed in the Frequency Domain is Better as Compared to the Data Processed in the Time Domain. A High Performance Accuracy with 97% is Obtained Comparing to an Accuracy Percentage of 83.13% were only Achieved by Using the Time Domain Data. Thus, this Paper has Successfully Made a Comparison between the Domains Proving the Reliability of the Frequency Domain for Wheeze Detection.

• Periodical: Applied Mechanics and Materials (Volume 591)
• Pages: 163-166
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.591.163
• Online since: July 2014
• Authors: Syamimi Mardiah Shaharum Syamimi Mardiah Shaharum*, Kenneth Sundaraj
• Keywords: K-Nearest Neighbor (KNN), Statistical Features, Wheeze Detection

References

[1] S. A. Taplidou, L. J. Hadjileontiadis, Wheeze detection based on time-frequency analysis of breath sounds, Computers in biology and medicine, 37(8), 1073-1083 (2007).

DOI: 10.1016/j.compbiomed.2006.09.007

[2] S. S. Alsmadi, Y. P. Kahya, Online classification of lung sounds using DSP, Proceedings of the Second Joint in Engineering in Medicine and Biology, 2, 1771-1772 (2002).

DOI: 10.1109/iembs.2002.1106645

[3] R. Jané, D. Salvatella, J. A. Fiz, J. Morera, Spectral analysis of respiratory sounds to assess bronchodilator effect in asthmatic patients, Proceedings of the 20th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, 3203-3206 (1998).

DOI: 10.1109/iembs.1998.746175

[4] E. Ç. Güler, B. Sankur, Y. P. Kahya, S. Raudys, Two-stage classification of respiratory sound patterns, Computers in Biology and Medicine, 35(1), 67-83 (2005).

DOI: 10.1016/j.compbiomed.2003.11.001

[5] J. C. Chien, H. D. Wu, F. C. Chong, C. I. Li, Wheeze detection using cepstral analysis in gaussian mixture models, 29th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, 3168-3171 (2007).

DOI: 10.1109/iembs.2007.4353002

[6] S. Le Cam, A. Belghith, C. Collet, F. Salzenstein, Wheezing sounds detection using multivariate generalized gaussian distributions, IEEE International Conference on  Acoustics, Speech and Signal Processing, 541-544 (2009).

DOI: 10.1109/icassp.2009.4959640

[7] M. J. Mussell, The need for standards in recording and analysing respiratory sounds, Medical and Biological Engineering and Computing, 30(2), 129-139 (1992).

DOI: 10.1007/bf02446121

[8] R. Jané, S. Cortes, J. A. Fiz, J. Morera, Analysis of wheezes in asthmatic patients during spontaneous respiration, 26th Annual International Conference of the IEEE  in Engineering in Medicine and Biology Society, 2, 3836-3839 (2004).

DOI: 10.1109/iembs.2004.1404074

[9] J. Peirick, Automated apnoea detection by computer: analysis of tracheal breath sounds, Medical and Biological Engineering and Computing, 21(5), 632-635 (1983).

DOI: 10.1007/bf02442390

[10] A. Cohen, D. Landsberg, Analysis and automatic classification of breath sounds, IEEE Transactions on Biomedical Engineering, 9, 585-590 (1984).

DOI: 10.1109/tbme.1984.325301

[11] M. S. Aldayel, K-Nearest Neighbor classification for glass identification problem, 2012 International Conference on  Computer Systems and Industrial Informatics, 1-5, (2012).

DOI: 10.1109/iccsii.2012.6454522

[12] Z. S. Nadiatun, P. M. D. Mashor, H. Nor Hazlyna, B. Fatimatul Anis, H. Rosline, Classification of blasts in acute leukemia blood samples using k-nearest neighbor, IEEE 8th International Colloquium on Signal Processing and its Application, 461-465 (2012).

DOI: 10.1109/cspa.2012.6194769

[13] J. A. Fiz, R. Jané, D. Salvatella, J. Izquierdo, L. Lores, P. Caminal, J. Morera, Analysis of Tracheal Sounds During Forced Exhalation in Asthma Patients and Normal Subjects Bronchodilator Response Effect, CHEST Journal, 116(3), 633-638 (1999).

DOI: 10.1378/chest.116.3.633

[14] J. Blitzer, K. Q. Weinberger, L. K. Saul, Distance metric learning for large margin nearest neighbor classification, Advances in neural information processing systems, 1473-1480 (2005).

[15] I. Saini, D. Singh, A. Khosla, Classification of RR-Interval and Blood Pressure for Different Postures using KNN Algorithm, International Journal of Signal Processing, Image Processing and Pattern Recognition, 1(5), 13-20 (2012).

DOI: 10.7763/ijcte.2012.v4.490

[16] M. Van Der Heijden , P. J. Lucas, B. Lijnse, Y. F. Heijdra, T. R. Schermer, An autonomous mobile system for the management of COPD, Journal of biomedical informatics, (2013).

DOI: 10.1016/j.jbi.2013.03.003