Diagnosing aortic valve stenosis by parameter extraction of heart sound signals.
The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.
Voss
Andreas
A
Department of Medical Engineering, University of Applied Sciences Jena, 07745 Jena, Germany. voss@fh-jena.de
Mix
Andrea
A
Hübner
Thomas
T
D001024:Aortic Valve Stenosis / Q000175:diagnosis* / Q000503:physiopathology; D003937:Diagnosis, Differential; D005583:Fourier Analysis; D006347:Heart Sounds; D006801:Humans; D008944:Mitral Valve Insufficiency / Q000175:diagnosis / Q000503:physiopathology; D010701:Phonocardiography / Q000295:instrumentation / Q000379:methods; D012680:Sensitivity and Specificity