Research Article

Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases

Table 2

Ten selected QRS detection algorithms.

MethodsFilteringExtracting featuresSetting thresholdPostprocessing

Pan–Tompkins algorithm [15]5–15 Hz band-pass filterDerivative; square; integrateTwo sets of adaptive thresholdsSearching back; T wave judging
Hamilton-mean algorithm [11]
Hamilton-median algorithm [11]
RS slope algorithm [2123]Median filterDerivative; detecting negative slope10 groups of duration empirical thresholds; one fixed amplitude threshold200 ms refractory blanking technology
Sixth power algorithm [24]Two-stage median filterSixth powerOne adaptive thresholdDetermining end point K
Finite state machine (FSM) algorithm [25]/Derivative; integrate; squareThree thresholding stages/
U3 transform algorithm (U3) [26]8–30 Hz band-pass filterU3 transformTwo fixed thresholdsSearching back; 270 ms refractory blanking technology
Difference operation algorithm (DOM) [2, 27]8–30 Hz band-pass filterDerivative; detecting positive extreme pointsPositive threshold; negative thresholdOptimizing; matching filtered signal
“jqrs” algorithm [2830]Sombrero hat-like low-pass filterIntegrateOne fixed thresholdSearching back; 200 ms refractory blanking technology
Optimized knowledge-based algorithm (OKB) [1]8–20 Hz band-pass filterSquaring; integrationTwo dynamic thresholdsDetermining the maxima of each block as R peak