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. 1990 Apr;37(4):366-73.
doi: 10.1109/10.52343.

Detection of coronary occlusions using autoregressive modeling of diastolic heart sounds

M Akay  1 , J L Semmlow, W Welkowitz, M D Bauer, J B Kostis
Affiliations

Detection of coronary occlusions using autoregressive modeling of diastolic heart sounds

M Akay et al. IEEE Trans Biomed Eng. 1990 Apr.

Abstract

Previous studies have indicated that diastolic heart sounds may contain information useful in the detection of occluded coronary arteries. In this study, recordings of diastolic heart sound segments were modeled by autoregressive (AR) methods including the adaptive recursive least-square lattice (RLSL) and the gradient lattice predictor (GAL). Application of the Akaike criterion demonstrated that between 5 and 15 AR coefficients are required to completely describe a diastolic segment. The reflection coefficients, prediction coefficients, zeros of the polynomial of the inverse filter, and the AR spectrum were determined over a number (N = 20-30) of diastolic segments. Preliminary results indicate that the averaged AR spectrum and the zeros of the inverse filter polynomial can be used to distinguish between normal patients and those with coronary artery disease.

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