Detection and delineation of the enigmatic U-wave in an electrocardiogram
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Abstract
Electrocardiogram (ECG) comprises of P-QRS-T wave components and sometimes U-wave. As per literature, the U-wave is said to be associated with certain cardiac disorders, but no more efforts have been made in developing techniques for detection of presence of U-wave and its delineation. The proposed technique presents a novel approach which can detect the presence of U-wave in the TP-segment and can delineate its peak. Features extracted from the TP-segment have been fed to random forest algorithm (RFA) and K-nearest neighbors (KNN) for classification. Sensitivity (Se%) = 96.66, specificity (Sp%) = 97.29, positive predictivity (+P%) = 97.31, Accuracy (Ac%) = 96.97, area under the receiver operating characteristic curve (Roc) = 0.994 using RFA and Se% = 96.23, Sp% = 94.70, +P% = 94.61, Ac% = 95.45, Roc = 0.973 using KNN have been achieved. The proposed technique also delineates the U-wave peak with an overall mean difference of 30.02 ms.
Keywords
Electrocardiogram (ECG) U-wave Random forest algorithm (RFA) K-nearest neighbors (KNN)Notes
Compliance with ethical standards
Conflict of interest
Authors have no conflict of interest.
References
- 1.Postema PG, van Eck HJ, Opthof T, van Herpen G, van Dessel PF, Priori SG et al (2009) I K1 modulates the U-wave: insights in a 100-year-old enigma. Heart Rhythm 6(3):393–400CrossRefGoogle Scholar
- 2.Riera AP, Ferreira C, Ferreira Filho C, Ferreira M, Meneghini A, Uchida AH et al (2008) The enigmatic sixth wave of the electrocardiogram: the U wave. Cardiol J 15(5):408–421Google Scholar
- 3.Levis JT (2012) ECG diagnosis: hypokalemia. Perm J 16(2):57CrossRefGoogle Scholar
- 4.Kirchhof P, Franz MR, Bardai A, Wilde AM (2009) Giant TU waves precede torsades de pointes in long QT syndrome: a systematic electro-cardiographic analysis in patients with acquired and congenital QT prolongation. JACC 54(2):143–149CrossRefGoogle Scholar
- 5.Hefer D, Bukharovich I, Nasrallah EJ, Plotnikov A (2005) Prominent positive U waves appearing with high-dose intravenous phenylephrine. Electrocardiol 38(4):378–382CrossRefGoogle Scholar
- 6.Conrath CE, Opthof T (2005) The patient U wave. Cardiovasc Res 67(2):184–186CrossRefGoogle Scholar
- 7.Verma N, Figueredo VM, Greenspan AM, Pressman GS (2011) Giant U waves: an important clinical clue. Res Rep Clin Cardiol 2:51Google Scholar
- 8.Gorshkov-Cantacuzne V (2016) To the question of the etiology and clinical significance of the U wave of the ECG. BNP 10:130–133Google Scholar
- 9.Vila JA, Gang Y, Presedo JM, Fernndez-Delgado M, Barro S, Malik M (2000) A new approach for TU complex characterization. IEEE Trans Biomed Eng 47(6):764–772CrossRefGoogle Scholar
- 10.Johannesenl L, Grovel USL, Sorensen1 JS, Schmidt1 ML, Couderc JP, Graff C (2010) A wavelet based algorithm for delineation and classification of wave patterns in continuous Holter ECG recordings. Comput Cardiol 2010; Belfast, UK, 26–29 September 2010Google Scholar
- 11.Laguna P, Mark RG, Goldberg A, Moody GB (1997) A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Comput Cardiol; IEEE: pp 673–676Google Scholar
- 12.Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG et al (2000) Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefGoogle Scholar
- 13.Sharma LD, Sunkaria RK (2016) A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement 87:194–204CrossRefGoogle Scholar
- 14.Sharma LD, Sunkaria RK (2017) Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process 12(2):199–206CrossRefGoogle Scholar
- 15.Sharma LD, Sunkaria RK (2018) Stationary wavelet transform based technique for automated external defibrillator using optimally selected classifiers. Measurement 125:29–36CrossRefGoogle Scholar
- 16.Strumillo P (2002) Nested median filtering for detecting T-wave offset in ECGs. Electron Lett 38(14):1CrossRefGoogle Scholar
- 17.Goutas A, Ferdi Y, Herbeuval JP, Boudraa M, Boucheham B (2005) Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation. ITBM-RBM 26(2):127–132CrossRefGoogle Scholar
- 18.Lin C, Kail G, Giremus A, Mailhes C, Tourneret JY, Hlawatsch F (2014) Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: block Gibbs sampler and marginalized particle filter. Signal Process 104:174–187CrossRefGoogle Scholar
- 19.Madeiro JP, Nicolson WB, Cortez PC, Marques JA, Vzquez-Seisdedos CR, Elangovan N et al (2013) New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model. Med Eng Phys 35(8):1105–1115CrossRefGoogle Scholar
- 20.Lin C, Mailhes C, Tourneret JY (2010) P-and T-wave delineation in ECG signals using a Bayesian approach and a partially collapsed Gibbs sampler. IEEE Trans Biomed Eng 57(12):2840–2849CrossRefGoogle Scholar
- 21.Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefzbMATHGoogle Scholar
- 22.Mitchell TM (1997) Machine Learning. McGraw-Hill Science, BostonzbMATHGoogle Scholar
- 23.Webb AR (2003) Statistical pattern recognition. Wiley, ChichesterzbMATHGoogle Scholar