Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals

Abstract

INTRODUCTION: Aortic stenosis (AS) is a severe complicated heart valve disease. This valve abnormality is a slow-progressive condition and mostly asymptomatic. Hence, there is a need for a rapid non-invasive AS diagnosis method with minimal feature extraction. 
 OBJECTIVE: In this paper, we proposed a rapid spectral analysis-based statistical feature extraction method to identify the AS stages with the minimum number of features.
 METHODS: In this study, the heart sound signals were collected from the medical database and transformed into the frequency domain for further spectral feature analysis. We used the windowing technique to condition the heart signals before spectral analysis. The spectral statistical features were extracted from the computed frequency spectrum. The range of statistical features was compared for normal, early, and delayed AS groups.
 RESULTS: In experiments, the normal, early, and delayed AS heart sound signals were used. The experimental results show the statistical difference between the normal and AS heart sound signal spectrums. The normal/unhealthy condition of a heart was identified using the statistical features of the frequency spectrum.
 CONCLUSION: The experimental results confirmed that the statistical features derived from the heart sound signal spectrums were varied according to the AS condition. Hence, the spectral statistical features can be considered as a rapid predictor of AS.