Lung Sounds Classification Using Spectrogram's First Order Statistics Features
Penulis/Author
Prof. Dr. Ir. Risanuri Hidayat, M.Sc., IPM. (1); Prof. Ir. Hanung Adi Nugroho, S.T., M.Eng., Ph.D., IPM., SMIEEE. (2)
Tanggal/Date
2016
Kata Kunci/Keyword
Abstrak/Abstract
Lung sounds can indicate a person's health condition.
Lung sounds are generated from the air flow in the respiratory
tract. Various of signal processing techniques are used for lung
sounds analysis to reduce the subjectivity of the lung sound
analysis. In this study, we propose lung sound signal analysis
using first order statistic texture analysis on the spectrogram.
The mean, variance, skewness, kurtosis, and entropy are used as
features of each lung sound. These features are analyzed using KNN
with two methods of distance measurement. The proposed
method achieves an accuracy of 96.3% for 81 data.
