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

David Pang,Tomohiko Igasaki

doi:10.3390/a11060083

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.

Highlights

Electrocardiography (ECG) is the graphical representation of the electrical activity of the heart over periods of time
Our goal is to develop a system for a real-time long-term heart rate variability (HRV) analysis of its usefulness, goal is to develop a system for a real-time long-term that Because can be embedded into a our bedside monitor for use in intensive care units or highHRV
We propose a new method of detecting R peaks from acquired long-term ECG data for use in HRV analysis

Summary

Introduction

Electrocardiography (ECG) is the graphical representation of the electrical activity of the heart over periods of time. ECG signals can generally be acquired through simple noninvasive recordings and manifest as a series of waves characterized by three main wave types: P, QRS, and T. The QRS complex is the most distinctive feature in ECG signals indicating the heartbeat, and its fiducial marker is the peak of the R wave (Figure 1). Heartbeat regulation is performed by the autonomic nervous system (ANS), which influences many vital organs in the body [1]. The heart rate variability (HRV) reflects the ANS activity, a relationship that provides considerable insight into many physiological and pathological factors that influence the normal heart rhythm [2,3,4]. Long-term HRV analysis has been proven helpful for clinical professionals in identifying autonomic impairment and providing prognoses of patient condition. The analysis can support the delivery of suitable medical treatments and prevent the development of diseases

Objectives

Methods

Conclusion