Distinguishing normal and abnormal tracheal breathing sounds by principal component analysis

Expired and inspired tracheal breathing sounds (BS) were recorded from 10 normal subjects and 8 patients with respiratory diseases, including bronchial asthma, sarcoidosis, fibrosing lung disease, chronic bronchitis, and radiation pneumonitis. Frequency spectra were generated using Fast Fourier Transform (FFT), and we observed considerable differences between BS spectra of normal subjects and patients. The frequency of peak amplitude and mean frequency of the BS spectra of patients were significantly higher than those of normal subjects. Spectral features were extracted by dividing each spectra into equal frequency bands--each feature being the mean amplitude of each FFT element within a frequency band. We used Principal Component Analysis to compare spectral feature sets and found a clear separation between normal and abnormal tracheal BS for 10, 20, and 40 features/spectra. We conclude that Principal Component Analysis of BS could become a new method of diagnosing respiratory disease in an automated fashion.