TY - GEN
T1 - Time-frequency analysis of wheezing sound to differentiate asthmatic and non-asthmatic condition
AU - Alang, Tengku Ahmad Iskandar T.
AU - Singh, Om Prakash
AU - Malarvili, M. B.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/5
Y1 - 2017/4/5
N2 - In this paper, a new method to analyze wheezing sound to differentiate asthmatic and non-asthmatic condition is proposed. To achieve this, data acquisition was done on asthmatic and non-asthmatic patients. The data was then filtered by using the high pass-Butterworth filter to obtain a smooth signal. Segmentation of expiration phase emphasized wheezing signal characteristic of the total of 60 epochs. The next step was the selection of time-frequency distribution (TFD) which enabled the feature extraction of frequency, maximum energy, and average energy. Based on comparison done, Modified-B distribution exhibited the best time-frequency resolution for this application. Extracted wheezing features from the time-frequency distribution of asthmatic and non-asthmatic conditions were subsequently analyzed using statistical analysis of t-test. The result indicates that the frequency can be used to differentiate asthmatic and non-asthmatic condition. In conclusion, the Modified-B distribution can distinguish asthmatic and non-asthmatic condition, based on frequency extraction.
AB - In this paper, a new method to analyze wheezing sound to differentiate asthmatic and non-asthmatic condition is proposed. To achieve this, data acquisition was done on asthmatic and non-asthmatic patients. The data was then filtered by using the high pass-Butterworth filter to obtain a smooth signal. Segmentation of expiration phase emphasized wheezing signal characteristic of the total of 60 epochs. The next step was the selection of time-frequency distribution (TFD) which enabled the feature extraction of frequency, maximum energy, and average energy. Based on comparison done, Modified-B distribution exhibited the best time-frequency resolution for this application. Extracted wheezing features from the time-frequency distribution of asthmatic and non-asthmatic conditions were subsequently analyzed using statistical analysis of t-test. The result indicates that the frequency can be used to differentiate asthmatic and non-asthmatic condition. In conclusion, the Modified-B distribution can distinguish asthmatic and non-asthmatic condition, based on frequency extraction.
KW - asthma and non-asthma
KW - Features
KW - Modified B-distribution
KW - t-test
KW - time-frequency distribution
UR - http://www.scopus.com/inward/record.url?scp=85018994735&partnerID=8YFLogxK
U2 - 10.1109/ICCSCE.2016.7893570
DO - 10.1109/ICCSCE.2016.7893570
M3 - Conference proceedings published in a book
AN - SCOPUS:85018994735
T3 - Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
SP - 193
EP - 198
BT - Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
Y2 - 25 November 2016 through 27 November 2016
ER -
