phat 22(4): e5

Research Article

Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals

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  • @ARTICLE{10.4108/eetpht.v8i4.3168,
        author={S. V. Mahesh Kumar and P. Dhinakar and R. Nishanth},
        title={Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={8},
        number={4},
        publisher={EAI},
        journal_a={PHAT},
        year={2022},
        month={9},
        keywords={Heart, Aortic stenosis, Heart sound, Frequency spectrum, Statistical features, Early detection},
        doi={10.4108/eetpht.v8i4.3168}
    }
    
  • S. V. Mahesh Kumar
    P. Dhinakar
    R. Nishanth
    Year: 2022
    Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals
    PHAT
    EAI
    DOI: 10.4108/eetpht.v8i4.3168
S. V. Mahesh Kumar1,*, P. Dhinakar1, R. Nishanth2
  • 1: Amrita College of Engineering and Technology, Nagercoil, India
  • 2: Department of Electronics and Communication Engineering, Cochin University College of Engineering Kuttanad, Kerala, India
*Contact email: maheshyesvee@gmail.com

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 diagnosis method with minimal feature extraction. OBJECTIVE: In this paper, we proposed a spectral features-based rapid heart sound signal analysis method to identify the AS stages with 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 conditioning 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 AS sound signals. RESULTS: In experiments, the normal, early, and delayed AS heart sound signals were used. The normal/unhealthy condition of a heart was identified using the statistical features of the frequency spectrum. The experimental results show the statistical difference between the normal and AS heart sound signal spectrums. 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 rapid predictors of AS.