Autoregressive Whitening Filtering of Phonocardiography Signals for Detection of Coronary Artery Disease

B. S. Larsen, Simon Winther, Louise Nissen, Axel Diederichsen, Morten Bøttcher, Johannes Struijk, Mads Græsbøll Christensen, S. Emil Schmidt

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Background: Narrowing of the coronary arteries, which defines coronary artery disease (CAD), can be discovered through phonocardiography (PCG) analysis. Increased power of frequencies below 200 Hz in the diastole has been associated with CAD, and can be used to distinguish CAD from NonCAD patients. However, spectral roll off is steep (~40 dB/dec), and spectral leakage might mask the weak CAD-related signal.Methods: PCGs from 1168 subjects, 213 CAD and 955 NonCAD, were pooled from three studies. The average power spectral density (PSD) of diastole segments for NonCAD subjects was found, and an auto-regressive (AR) model of this PSD was constructed. The inverse of the corresponding filter was used for whitening.Results: A single iteration of whitening filtering was insufficient to make the PSD white for 5-1000 Hz. Two iterations of whitening filtering with an order of 6-10 were required to reach a plateau of maximal whitening with a spectral flatness measure close to 1 in the frequency band 5-1000 Hz. The whitening process revealed additional PSD differences between CAD and NonCAD subjects for the mid-diastole segment.Conclusion: Whitening of diastole PCG segments emphasized the difference between CAD and NonCAD patients.

Titel2019 Computing in Cardiology, CinC 2019
Publikationsdatosep. 2019
ISBN (Trykt)978-1-7281-5942-3
ISBN (Elektronisk)978-1-7281-6936-1
StatusUdgivet - sep. 2019
Begivenhed2019 Computing in Cardiology - , Singapore
Varighed: 8. sep. 201911. sep. 2019


Konference2019 Computing in Cardiology


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