Abstract
This paper considers the novel QRS-detector of ECG signal based on the consecutive application of band-pass filtering, Hilbert transform and adaptive thresholding. The robustness of various QRS-detectors for processing model ECG signals to the presence of intensive noises and artifacts was researched. The performance of the proposed method as well as some other established and well-known algorithms for QRS-detection was further verified for different recordings of clinical ECG signals from the Physionet MIT-BIH Arrhythmia database.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation and clinical use (1996). Circulation 93(5):1043-1065
Friesen G. M et al. (1990) A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering 27(1):85–98
Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC. (1993) Edited by Willis J. Tompkins. Prentice Hall, New York
Theis F. J., Meyer-Base A. (2010) Biomedical signal analysis: Contemporary methods and applications. The MIT Press, Cambridge
Mohamed E. et al. (2010) Frequency Bands Effects on QRS Detection, Proc. of the 3rd International Conference on Bioinspired Systems and Signal Processing, Valencia, Spain, 2010, pp 428-431
Fedotov A. A., Akulova A. S., Akulov S. A. (2015) Analysis of the parameters of frequency filtering of an electrocardiograph signal. Springer: Measurement Techniques 57(11):1320-1325
Benitez D. et al (2001) The use of the Hilbert transform in ECG signal analysis. Computers in Biology and Medicine 31: 399–406
Rangayyan R. M. (2002) Biomedical Signal Analysis: A Case-Study Approach. IEEE Press and Wiley, New York
McSharry P. E. et al. (2003) A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3):289–295
Han H. (2007) Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography, Proc. of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, 2007, pp 1539–1541
Pan J., Tompkins W. J. (1985) A real time QRS detection algorithm. IEEE Transactions on Biomedical Engineering 32:230-236
Ruha A., Sallinen S., Nissila S. (1997) A Real-Time Microprocessor QRS Detector System with a 1-ms Timing Accuracy for the Measurement of Ambulatory HRV. IEEE Transactions on Biomedical Engineering 44(3):159–167
Kadambe S., Murray R. et al. (1999) Wavelet transform based QRS complex Detector. IEEE Transactions on Biomedical Engineering 46(7):838–848
Moody G. B., Mark R. G. (2001) The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology 20(3):45-50
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Fedotov, A.A., Akulova, A.S., Akulov, S.A. (2016). Effective QRS-Detector Based on Hilbert Transform and Adaptive Thresholding. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)