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Real-Time Robust Heart Rate Estimation Based on Bayesian Framework and Grid Filters
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Author(s): Radoslav Bortel (Czech Technical University in Prague, Czech Republic)and Pavel Sovka (Czech Technical University in Prague, Czech Republic)
Copyright: 2012
Pages: 24
Source title:
Medical Applications of Intelligent Data Analysis: Research Advancements
Source Author(s)/Editor(s): Rafael Magdalena-Benedito (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Emilio Soria-Olivas (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Guerrero Martínez (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Gómez-Sanchis (Intelligent Data Analysis Laboratory, University of Valencia, Spain)and Antonio Jose Serrano-López (Intelligent Data Analysis Laboratory, University of Valencia, Spain)
DOI: 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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