Real time electrocardiogram QRS detection using combined adaptive threshold

TLDR

QRS and ventricular beat detection is a fundamental ECG processing task, and numerous methods have achieved high detection rates. The study proposes a real‑time QRS detection method that compares the absolute values of summed differentiated ECGs across leads against an adaptive threshold. The adaptive threshold incorporates a slew‑rate term, a noise‑sensitive component, and a low‑amplitude guard, and two self‑adjusting algorithms—one detecting the current beat and another adding RR‑interval analysis—operate with any number of leads and adapt to beat‑to‑beat intervals. On the MIT‑BIH arrhythmia database, the algorithms achieved sensitivities of 99.69 % and 99.74 % and specificities of 99.65 % for Algorithms 1 and 2, respectively, matching or exceeding published performance.

Abstract

QRS 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 ECGsA 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.The 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.The statistical indices are higher than, or comparable to those, cited in the scientific literature.