Real time electrocardiogram QRS detection using combined adaptive threshold

By I. Christov. BioMedical Engineering OnLine, 2004

483 citations recorded by the source corpus.

BackgroundQRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGsMethodsA real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats.Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition.The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number L of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals.ResultsThe algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity Se = 99.69 % and specificity Sp = 99.65 % for Algorithm 1 and Se = 99.74 % and Sp = 99.65 % for Algorithm 2.ConclusionThe statistical indices are higher than, or comparable to those, cited in the scientific literature.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0306b922360515874aae81974331de72aa1ef8c4

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

Removal of FMRI environment artifacts from EEG data using optimal basis sets (2005, methodology): “We have shown the application of a heuristic algorithm (Christov, 2004) for ECG detection which achieves sensitivity and specificity that rival algorithms applied to multi-lead ECG recordings conducted outside the scanner.”
Depression and Anxiety (2008, unknown): “Bad ECG signal fragments (artifacts) were automatically detected, after which a modified version of the algorithm by Christov (Christov, 2004) was used to detect R‐wave peaks.”
Analysis of First-Derivative Based QRS Detection Algorithms (2008, unknown): “Finally, although the use of multiple recording leads could improve detection accuracy especially when combined with multiple adaptive components in the detection algorithm [ 16 ], we chose to benchmark cost-effective detection algorithms that are applicable to single-lead ECG recordings.”
Gaussian Process Robust Regression for Noisy Heart Rate Data (2008, background): “Several authors have considered automated strategies for inferring heart rate and other statistics from recordings of the full ECG waveform [1]–[3] or from the complete sequence of IBI [4]–[6].”
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases (2012, methodology): “The heuristic methods [10–12] were also widely used in classifying the ECG signals.”