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R-Peak Estimation using Multimodal Lead Switching

Abstract : Introduction: Intensive care unit patients are heavily monitored, and a number of clinically relevant parameters are routinely extracted from high resolution signals. In particular, heart rate is derived from intervals between pulses in pseudo-periodic signals such as the electrocardiogram (ECG) or arterial blood pressure (ABP) waveforms. However, poor signal quality and high noise levels can unfortunately lead to false localisation of these pulses (or peaks), resulting in incorrect estimates of heart rate. The goal of the 2014 Physionet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different biosignals to create a robust set of peak detections. Methods: First, a set of peak detectors were evaluated on different cardiovascular signals. The detections were then fused using two different approaches: the first one was based on a calculated measures of signal quality for the ECG and ABP signals and the the second fusion technique was based on the regularity of the derived intervals between subsequent detections made on ECG, ABP, Stroke Volume and Photoplethysmogram signals. These techniques were developed using the MGH/MF database and submitted for scoring on the Challenge test-set. Conclusion: The best entries for the two approaches obtained an overall score of 87.88% and 87.66%, respectively, in phase III of the challenge, which provided the highest official score.
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Submitted on : Monday, March 19, 2018 - 12:11:06 PM
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  • HAL Id : hal-01737182, version 1



Alistair Ew Johnson, Joachim Behar, Fernando Andreotti, Gari D. Clifford, Julien Oster. R-Peak Estimation using Multimodal Lead Switching. 2014 Computing in Cardiology Conference (CinC 2014) , Sep 2014, Cambridge, MA, United States. pp.281-284. ⟨hal-01737182⟩



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