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Algorithms 2018, 11(6), 83; https://doi.org/10.3390/a11060083

A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis

1
Department of Human and Environmental Informatics, Graduate School of Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
2
Field of Biomedical and Welfare Engineering, Division of Informatics and Energy, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 23 May 2018 / Accepted: 5 June 2018 / Published: 7 June 2018
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Abstract

Long-term heart rate variability (HRV) analysis is useful as a noninvasive technique for autonomic nervous system activity assessment. It provides a method for assessing many physiological and pathological factors that modulate the normal heartbeat. The performance of HRV analysis systems heavily depends on a reliable and accurate detection of the R peak of the QRS complex. Ectopic beats caused by misdetection or arrhythmic events can introduce bias into HRV results, resulting in significant problems in their interpretation. This study presents a novel method for long-term detection of normal R peaks (which represent the normal heartbeat in electrocardiographic signals), intended specifically for HRV analysis. The very low computational complexity of the proposed method, which combines and exploits the advantages of syntactical and statistical approaches, enables real-time applications. The approach was validated using the Massachusetts Institute of Technology–Beth Israel Hospital Normal Sinus Rhythm and the Fantasia database, and has a sensitivity, positive predictivity, detection error rate, and accuracy of 99.998, 99.999, 0.003, and 99.996%, respectively. View Full-Text
Keywords: electrocardiogram (ECG); real-time R peak detection; long-term heart rate variability (HRV) analysis; automata; normal sinus rhythm (NSR) electrocardiogram (ECG); real-time R peak detection; long-term heart rate variability (HRV) analysis; automata; normal sinus rhythm (NSR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pang, D.; Igasaki, T. A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis. Algorithms 2018, 11, 83.

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