Carotid bruits are systolic sounds associated with turbulent blood flow through atherosclerotic stenosis in the neck. They are audible intermittent high-frequency (above 200 Hz) sounds mixed with background noise and transmitted low-frequency (below 100 Hz) heart sounds that wax and wane periodically. It is a nontrivial task to extract both bruits and heart sounds with high fidelity for further computer-aided auscultation and diagnosis. In this paper we propose a rolling ball sifting algorithm that is capable to filter signals with a sharper frequency selectivity mechanism in the time domain. By rolling two balls (one above and one below the signal) of a suitable radius, the balls are large enough to roll over bruits and yet small enough to ride on heart sound waveforms. The high-frequency bruits can then be extracted according to a tangibility criterion by using the local extrema touched by the balls. Similarly, the low-frequency heart sounds can be acquired by a larger radius. By visualizing the periodicity information of both the extracted heart sounds and bruits, the proposed visual inspection method can potentially improve carotid bruit diagnosis accuracy.
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