Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings

  • Carlos A. Ledezma
  • Miguel AltuveEmail author
Original Article


The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation

Graphical Abstract

N QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β)



QRS complex detection Electrocardiography Digital filters Optimal data fusion Detection algorithms 


Supplementary material

11517_2019_1990_MOESM1_ESM.docx (115 kb)
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  1. 1.
    Almeida R, Martínez JP, Rocha AP, Laguna P (2009) Multilead ECG delineation using spatially projected leads from wavelet transform loops. IEEE Trans Biomed Eng 56(8):1996–2005CrossRefGoogle Scholar
  2. 2.
    American National Standard: ANSI/AAMI EC38:1998, Ambulatory Electrocardiographs (1998)Google Scholar
  3. 3.
    American National Standard: ANSI/AAMI EC57:1998, Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms (1998)Google Scholar
  4. 4.
    Arbateni K, Bennia A (2014) Sigmoidal radial basis function ANN for QRS complex detection. Neurocomputing 145:438–450CrossRefGoogle Scholar
  5. 5.
    Benitez DS, Gaydecki PA, Zaidi A, Fitzpatrick AP (2000) A new QRS detection algorithm based on the Hilbert transform. In: Computers in cardiology 2000, pp 379–382Google Scholar
  6. 6.
    Chair Z, Varshney P.K. (1986) Optimal data fusion in multiple sensor detection systems. IEEE Trans Aerosp Electron Syst AES-22(1):98–101CrossRefGoogle Scholar
  7. 7.
    Chouhan V, Mehta S (2008) Detection of QRS complexes in 12-lead ECG using adaptive quantized threshold. Int J Comp Sci Net Sec 8(1):155–163Google Scholar
  8. 8.
    Christov II (2004) Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online 3(1):28CrossRefGoogle Scholar
  9. 9.
    Elgendi M, Björn E, Socrates D, Derek A (2014) Revisiting QRS detection methodologies for portable, wearable, Battery-Operated, and wireless ECG systems. PLOS ONE 9(1):1–18CrossRefGoogle Scholar
  10. 10.
    Ghaffari A, Homaeinezhad M, Akraminia M, Atarod M, Daevaeiha M (2009) A robust wavelet-based multi-lead electrocardiogram delineation algorithm. Med Eng Phys 31(10): 1219–1227CrossRefGoogle Scholar
  11. 11.
    Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefGoogle Scholar
  12. 12.
    He R, Wang K, Li Q, Yuan Y, Zhao N, Liu Y, Zhang H (2017) A novel method for the detection of R-peaks in ECG based on K-nearest neighbors and particle swarm optimization. EURASIP J Adv Signal Process 2017:82CrossRefGoogle Scholar
  13. 13.
    Huang B, Wang Y (2009) Detecting QRS complexes of two-channel ECG signals by using combined wavelet entropy. In: 2009 3Rd international conference on bioinformatics and biomedical engineering, pp 1–4Google Scholar
  14. 14.
    Huang B, Wang Y (2009) QRS complexes detection by using the principal component analysis and the combined wavelet entropy for 12-lead electrocardiogram signals. In: 2009 Ninth IEEE international conference on computer and information technology, vol 1, pp 246–251Google Scholar
  15. 15.
    Jain S, Ahirwal M, Kumar A, Bajaj V, Singh G (2017) QRS detection using adaptive filters: a comparative study. ISA Trans 66:362–375CrossRefGoogle Scholar
  16. 16.
    Laguna P, Jané R., Caminal P (1994) Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput Biomed Res 27(1):45–60CrossRefGoogle Scholar
  17. 17.
    Ledezma CA, Perpinan G, Severeyn E, Altuve M (2015) Data fusion for QRS complex detection in multi-lead electrocardiogram recordings. In: 11th international symposium on medical information processing and analysis, vol 9681. International Society for Optics and Photonics, p 968118Google Scholar
  18. 18.
    Mehta S, Lingayat N (2008) Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM. Comput Biol Med 38(1):138–145CrossRefGoogle Scholar
  19. 19.
    Mehta S, Lingayat N (2009) Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram. Comput Methods Prog Biomed 93(1):46–60CrossRefGoogle Scholar
  20. 20.
    Mehta S, Lingayat N (2009) Identification of QRS complexes in 12-lead electrocardiogram. Expert Syst Appl 36(1):820–828CrossRefGoogle Scholar
  21. 21.
    Meyer C, Gavela JF, Harris M (2006) Combining algorithms in automatic detection of QRS complexes in ECG signals. IEEE Trans Inf Technol Biomed 10(3):468–475CrossRefGoogle Scholar
  22. 22.
    Mondelo V, Lado MJ, Méndez AJ, Vila XA, Rodríguez-linares L (2017) Combining 12-lead ECG information for a beat detection algorithm. J Adv Theoret Appl Inform 3(1):5–9CrossRefGoogle Scholar
  23. 23.
    Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50CrossRefGoogle Scholar
  24. 24.
    Moraes JCTB, Freitas MM, Vilani FN, Costa EV (2002) A QRS complex detection algorithm using electrocardiogram leads. In: Computers in cardiology, pp 205–208Google Scholar
  25. 25.
    Nayak C, Saha SK, Kar R, Mandal D (2019) An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal. Biomed Signal Process Control 49:440–464CrossRefGoogle Scholar
  26. 26.
    Okada M (1979) A digital filter for the ORS complex detection. IEEE Trans Biomed Eng BME-26(12):700–703CrossRefGoogle Scholar
  27. 27.
    Pahlm O, Sörnmo L (1984) Software QRS detection in ambulatory monitoring – a review. Med Biol Eng Comput 22(4):289–297CrossRefGoogle Scholar
  28. 28.
    Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng BME-32 (3):230–236CrossRefGoogle Scholar
  29. 29.
    Ramakrishnan AG, Prathosh P, Ananthapadmanabha TV (2014) Threshold-Independent QRS detection using the dynamic plosion index. IEEE Signal Process Lett 21(5):554–558CrossRefGoogle Scholar
  30. 30.
    Rincón F, Recas J, Khaled N, Atienza D (2011) Development and evaluation of multilead wavelet-based ECG delineation algorithms for embedded wireless sensor nodes. IEEE Trans Inf Technol Biomed 15(6):854–863CrossRefGoogle Scholar
  31. 31.
    Silva I, Moody B, Behar J, Johnson A, Oster J, Clifford G D, Moody GB (2015) Robust detection of heart beats in multimodal data. Physiol Meas 36(8):1629CrossRefGoogle Scholar
  32. 32.
    Silva I, Moody GB (2014) An open-source toolbox for analysing and processing physionet databases in MATLAB and octave. J Open Res Soft 2(1):e27Google Scholar
  33. 33.
    Thakor NV, Webster JG, Tompkins WJ (1983) Optimal QRS detector. Med Biol Eng Comput 21 (3):343–350CrossRefGoogle Scholar
  34. 34.
    Torbey S, Akl SG, Redfearn DP (2012) Multi-lead QRS detection using window pairs. In: 2012 annual international conference of the IEEE engineering in medicine and biology society, pp 3143–3146Google Scholar
  35. 35.
    Willems JL, Arnaud P, Bemmel JHV, Bourdillon PJ, Degani R, Denis B, Graham I, Harms F M, Macfarlane PW, Mazzocca G, Meyer J, Zywietz C (1987) A reference data base for multilead electrocardiographic computer measurement programs. J Am Coll Cardiol 10(6):1313–1321CrossRefGoogle Scholar
  36. 36.
    Xiang Y, Lin Z, Meng J (2018) Automatic QRS complex detection using two-level convolutional neural network. BioMedical Engineering OnLine 17(1):13CrossRefGoogle Scholar
  37. 37.
    Yochum M, Renaud C, Jacquir S (2016) Automatic detection of p, QRS and T patterns in 12 leads ECG signal based on CWT. Biomed Signal Process Control 25:46–52CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity College LondonLondonUK
  2. 2.Faculty of Electrical and Electronic EngineeringPontifical Bolivarian UniversityBucaramangaColombia

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