A QRS complex detection algorithm using electrocardiogram leads

Abstract

A QRS complex detection algorithm was developed using the available l e a b of the electrocardiogram (ECG). This detector is based on the combination of two improved versions of QRS detectors available in the literature. An important characteristic of this algorithm is the possibility of using two or more ECG channels for QRS detection. The first detection method is based on a cross number in a detection threshold defined by the authors. When a low reliability situation occurs in the first method, the output of the second detection method is used to confirm or reject the detection. The second method also uses an adaptive detection threshold dejked by the authors and a candidate QRS is tested against some criteria that use features as amplitude, width and RR interval to validate the candidate as a QRS. Testing the algorithm with MIT/BIH Arrhythmia Database resulted in 99.22% sensitivity and 99.73% positive predictivity.

Real time electrocardiogram QRS detection using combined adaptive threshold

“…[1] combined logically two different algorithms working in parallel – the first has been taken from the work of Englese and Zeelenberg [7] and the other was based on Pan and Tompkins [8], and Ligtenberg and Kunt [9]. Moraes et al .…”

“…Moraes et al . [1] reported Se = 99.22 % and Sp = 99.73 % after having excluded records of patients with pacemaker. After excluding a few more recordings 108, 200, 201 and 203, containing high amplitude noise (according to the authors), the statistical indices rises to Se = 99.56 % and Sp = 99.82 %.…”

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

“…For example, researchers used 5–15 Hz [36], 5–11 Hz [4,5], 4–13.5 Hz [37], 4.1–33.1 Hz [38], 9–30 Hz [39] and 2.2–33.3 Hz [40] as the optimal frequency band to detect QRS complexes in ECG signals. However, the proposed TERMA method extracts the optimal frequency band during the training stage through a rigorous brute force optimization, which is 8–20 Hz in this case, as discussed above.…”

A assessment of multiscale-based peak detection algorithm using MIT/BIH Arrhythmia Database

“…일반적으로 사용되고 있는 피크 검출 방법에는 대표적으로 역치값 설정 기술(Adaptive threshold) [3], 미분(Differentiation) [8], 웨이블 렛 변환(Wavelet transform) [2], 힐버트 변환(Hilbert transform) [ 11Hz, Cuiwei [9] 8～58.8Hz, Sahambi [10] 3～40Hz, Benitez [11] 8～20Hz, Moraes [12] 9～30Hz, Mahmoodabadi [13] …”