Real-time R-wave Detection Research Articles

An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis.

R-wave detection is a prerequisite for the extraction and recognition of ECG signal feature parameters. In the analysis and diagnosis of exercise electrocardiograms, accurate and real-time detection of QRS complexes is very important for the prevention and monitoring of heart disease. This paper proposes a lightweight R-wave real-time detection method for exercise ECG signals. After real-time denoising of the exercise ECG signal, the median line is used to correct the baseline, and the first-order difference processing is performed on the differential square signal. Max-Min Threshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal. The abovementioned method was verified by using the measured data in the MIT-BIH ECG database of the Massachusetts Institute of Technology and the exercise plate experiment. The R-wave detection rates were 99.93% and 99.98%, respectively. Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring.

A real-time R-wave detection method for wireless ambulatory ECG telemetry

Wireless ambulatory ECG telemetry has been widely used in heart rate monitoring in altered gravity conditions, evaluation of high pressure nervous syndrome (HPNS), etc. Comparing with resting ECG monitoring in the hospital, ambulatory ECG features motion artefacts caused by limb movements, including peak interference, instantaneous distortion and amplitude saltation of QRS complex. Beside, in wireless networks, packets are probably dropped because of RF range interference and complex testing environment. To solve these problems, the paper proposes a novel real-time R-wave detection method for wireless ambulatory ECG telemetry (DET). Three sequences of ambulatory ECG were measured and analysed by a custom-made bioelectricity wireless telemetry system. The overall sensitivity, positive predictivity and failed detection were 99.7%, 99.9% and 0.47%, respectively.

Novel real-time R-wave detection algorithm based on the vectorcardiogram for accurate gated magnetic resonance acquisitions.

Electrocardiograph (ECG) triggered or gated magnetic resonance methods are used in many imaging applications. Therefore, a reliable trigger signal derived from to the R-wave of the ECG is essential, especially in cardiac imaging. However, currently available methods often fail mainly due to the artifacts in the ECG generated by the MR scanner itself, such as the magnetohydrodynamic effect and gradient switching noise. The purpose this study was to characterize the accuracy of selected R-wave detection algorithms in an MR environment, and to develop novel approaches to eliminate imprecise triggering. Vectorcardiograms (VCG) in 12 healthy volunteers exposed to 1.5 T magnetic field were digitized and used as a reference data set including manually corrected onsets of R-waves. To define the magnetohydrodynamic effect, the VCGs were characterized in time, frequency, and spatial domains. The selected real-time R-wave detection algorithms, and a new "target-distance" VCG-based algorithm were applied either to standard surface leads calculated from the recorded VCG or to the VCG directly. The flow related artifact was higher in amplitude than the R-wave in 28% of the investigated VCGs which yielded up to 9-16%false positive detected QRS complexes for traditional algorithms. The "target-distance" R-wave detection algorithm yielded a score of 100% for detection with 0.2% false positives and was superior to all the other selected methods. Thus, the VCG of subjects exposed to a strong magnetic field can be use to separate the magnetohydrodynamic artifact and the actual R-wave, and markedly improves the trigger accuracy in gated magnetic resonance scans. Magn Reson Med 42:361-370, 1999.