Enhancing coronary Wave Intensity Analysis robustness by high order central finite differences

Simone Rivolo, Kaleab N Asrress, Amedeo Chiribiri, Eva Sammut, Roman Wesolowski, Lars Ø Bloch, Anne K Grøndal, Jesper L Hønge, Won Y Kim, Michael Marber, Simon Redwood, Eike Nagel, Nicolas P Smith, Jack Lee

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)


BACKGROUND: Coronary Wave Intensity Analysis (cWIA) is a technique capable of separating the effects of proximal arterial haemodynamics from cardiac mechanics. Studies have identified WIA-derived indices that are closely correlated with several disease processes and predictive of functional recovery following myocardial infarction. The cWIA clinical application has, however, been limited by technical challenges including a lack of standardization across different studies and the derived indices' sensitivity to the processing parameters. Specifically, a critical step in WIA is the noise removal for evaluation of derivatives of the acquired signals, typically performed by applying a Savitzky-Golay filter, to reduce the high frequency acquisition noise.

METHODS: The impact of the filter parameter selection on cWIA output, and on the derived clinical metrics (integral areas and peaks of the major waves), is first analysed. The sensitivity analysis is performed either by using the filter as a differentiator to calculate the signals' time derivative or by applying the filter to smooth the ensemble-averaged waveforms. Furthermore, the power-spectrum of the ensemble-averaged waveforms contains little high-frequency components, which motivated us to propose an alternative approach to compute the time derivatives of the acquired waveforms using a central finite difference scheme.

RESULTS AND CONCLUSION: The cWIA output and consequently the derived clinical metrics are significantly affected by the filter parameters, irrespective of its use as a smoothing filter or a differentiator. The proposed approach is parameter-free and, when applied to the 10 in-vivo human datasets and the 50 in-vivo animal datasets, enhances the cWIA robustness by significantly reducing the outcome variability (by 60%).

Original languageEnglish
Pages (from-to)98-109
Number of pages12
JournalArtery Research
Issue number3
Publication statusPublished - Sep 2014


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