High sensitivity experimental QRS detector

This paper presents an approach to QRS complex detection in ECG signals using Hilbert transform and zero-phase filters to locate the R wave peaks. A newly proposed peak equalization and normalization method is used for signal conditioning prior to the fixed threshold peak detector. The performance of this algorithm is tested using standard ECG waveform records from the MIT-BIH arrhythmia database achieving average sensitivity of 99.95%.

[1]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.

[2]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[3]  W.J. Tompkins,et al.  Neural-network-based adaptive matched filtering for QRS detection , 1992, IEEE Transactions on Biomedical Engineering.

[4]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[5]  Myoungho Lee,et al.  A simple real-time QRS detection algorithm , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

[7]  Yang Liu,et al.  A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization , 2017, EURASIP Journal on Advances in Signal Processing.

[8]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[9]  Jianqing Li,et al.  An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm , 2017, Journal of healthcare engineering.