P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference
Tóm tắt
Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32±12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.
Từ khóa
Tài liệu tham khảo
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Ghaffari, 2009, robust wavelet - based multi - lead electrocardiogram delineation algorithm, Med Eng Phys, 31, 1219, 10.1016/j.medengphy.2009.07.017
Maan, 2014, Impact of catheter ablation on wave parameters on lead electrocardiogram in patients with atrial fibrillation, J Electrocardiol, 12, 725, 10.1016/j.jelectrocard.2014.04.010
Lin, 2011, wave delineation and waveform estimation in ECG signals using a block sampler In International on and Processing New York, IEEE Conference Acoustics Speech Signal ICASSP IEEE, 537
Haissaguerre, 1998, Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins, Engl J Med, 339
Karimipour, 2014, MR Real - time electrocardiogram detection - delineation algorithm based on quality - supported analysis of characteristic templates, Comput Biol Med, 153, 10.1016/j.compbiomed.2014.07.002
Schreier, 2001, An automatic ECG processing algorithm to identify patients prone to paroxysmal atrial fibrillation, Comput Cardiol, 28, 133
Vullings HJLM, 1998, Automated ECG segmentation with dynamic time warping In Proceedings of the th Annual International Conference of the in Medicine and Biology, IEEE Engineering Society, 20, 163
Murthy, 1992, Component wave delineation of ECG by filtering in the fourier domain, Med Biol Eng Comput, 30, 169, 10.1007/BF02446127
Goldberger, 2000, LAN PhysioNet components of a new research resource for complex physiologic signals, Circulation, 101
Daubechies, 1990, The wavelet transform time - frequency localization and signal analysis Theory, IEEE Trans Inform, 961, 10.1109/18.57199
Lee, wave morphology in guiding the ablation strategy of focal atrial tachycardias and atrial flutter, Curr Cardiol Rev, 2015
Schuster, 2005, EKG Kurs für Stuttgart Georg Verlag
Schilling, 2009, Non - linear energy operator for the analysis of intracardial electrograms InIFMBE Proceedings World Congress on and volume, Medical Physics Biomedical Engineering, 25, 872
Schilling, 2012, of atrial electrograms thesis Karlsruhe Institute of Technology KIT Karlsruhe, Analysis
Cabasson, 2011, wave indices to detect susceptibility to atrial fibrillation, Comput Cardiol, 257
Dössel, 2013, Lenis Baas beats and their influence on the morphology of subsequent waves in the electrocardiogram Technik, Biomed Eng, 109
Alcaraz, Role of the wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation, Comput Methods Prog, 2015
Martínez, 2004, wavelet based delineator evaluation on standard databases, IEEE Trans Biomed Eng, 570, 10.1109/TBME.2003.821031
Nason, 1995, The stationary wavelet transform and some statistical applications In Wavelets New York, statistics USA, 281
Goette, 1996, Langberg Electrical remodeling in atrial fibrillation time course and mechanisms, Circulation, 2968, 10.1161/01.CIR.94.11.2968
Haissaguerre, 1998, Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins, Engl J Med, 339
Dössel, 2013, Lenis Baas beats and their influence on the morphology of subsequent waves in the electrocardiogram Technik, Biomed Eng, 109
Stewart, 2004, Cost of an emerging epidemic : an economic analysis of atrial fibrillation in the uk, Heart, 286, 10.1136/hrt.2002.008748