Real-Time Robust Heart Rate Estimation Based on Bayesian Framework and Grid Filters

Real-Time Robust Heart Rate Estimation Based on Bayesian Framework and Grid Filters

Radoslav Bortel, Pavel Sovka
ISBN13: 9781466618039|ISBN10: 1466618035|EISBN13: 9781466618046
DOI: 10.4018/978-1-4666-1803-9.ch005
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MLA

Bortel, Radoslav, and Pavel Sovka. "Real-Time Robust Heart Rate Estimation Based on Bayesian Framework and Grid Filters." Medical Applications of Intelligent Data Analysis: Research Advancements, edited by Rafael Magdalena-Benedito, et al., IGI Global, 2012, pp. 67-90. https://doi.org/10.4018/978-1-4666-1803-9.ch005

APA

Bortel, R. & Sovka, P. (2012). Real-Time Robust Heart Rate Estimation Based on Bayesian Framework and Grid Filters. In R. Magdalena-Benedito, E. Soria-Olivas, J. Martínez, J. Gómez-Sanchis, & A. Serrano-López (Eds.), Medical Applications of Intelligent Data Analysis: Research Advancements (pp. 67-90). IGI Global. https://doi.org/10.4018/978-1-4666-1803-9.ch005

Chicago

Bortel, Radoslav, and Pavel Sovka. "Real-Time Robust Heart Rate Estimation Based on Bayesian Framework and Grid Filters." In Medical Applications of Intelligent Data Analysis: Research Advancements, edited by Rafael Magdalena-Benedito, et al., 67-90. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1803-9.ch005

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Abstract

In this chapter, the authors discuss derivation, implementation, and testing of a robust real-time algorithm for the estimation of heart rate (HR) from electrocardiograms recorded on subjects performing vigorous physical activity. They formulate the problem of HR estimation as a problem of inference in a Bayesian network, which utilizes prior information about the probability distribution of HR changes. From this formulation they derive an inference procedure, which can be implemented as a grid filter. The resulting algorithm can then follow even a rapidly changing HR, whilst withstanding a series of missed or false QRS detections. Also, the HR estimate is complete with confidence intervals to allow the identification of the moments, where the precision of HR estimation is lowered. Additionally, the computational complexity of this algorithm is acceptable for battery powered portable devices.

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