Heart Rate Variability and T Wave Alternans as risk stratification indices for detecting Sudden Cardiac Death: A Review

Authors

Keywords:

ECG, SCD, HRV, TWA, DETECTION

Abstract

Sudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Often, the symptoms
appear in healthy persons one hour before the fatal event. The incomprehensible nature of this cardiac disease increases the necessity to develop new methods to predict this pathology. A review of state of the art based on Kitchenham procedures is carried out. According to the literature review, several methods to predict SCD have been developed using Heart Rate Variability (HRV), and T-wave alternans (TWA). These indexes have been considered important, non-invasive, and promising indicators to stratify the risk of SCD. In this context, the work presented in this paper shows a review of the state of the art, focused on the research efforts about risk stratification indices based on HRV and TWA. The purpose of this review is developing a future research framework to stratify the risk of SCD disease, by mixing caracteristics of the HRV and TWA approaches, thus producing a new hybrid method.

Downloads

Download data is not yet available.

Author Biographies

Nancy C. Betancourt M., Universidad de las Fuerzas Armadas - ESPE /Escuela Politécnica Nacional

Nancy Betancourt es Master en Inteligencia Web por la Universidad Jean Monnet (Francia). Estudiante de Doctorado por la Escuela Polit.cnica Nacional (Ecuador) en el área de Sistemas Inteligentes. Actualmente, es profesora en la Universidad de las Fuerzas Armadas - ESPE (Ecuador). Trabaja en las áreas de Inteligencia Artificial y Procesamiento Digital de Señales.

Carlos Almeida, Escuela Politécnica Nacional

Carlos Almeida es profesor en la Escuela Politécnica Nacional. Sus intereses de investigación involucran, pero no exclusivamente, Estadística Matemática y Aplicada, e Inteligencia Artificial. Obtuvo su doctorado en Bélgica y durante varios años trabajó en Alemania y Bélgica antes de regresar a Ecuador.

Marco Flores-Calero, Universidad de las Fuerzas Armadas

Marco Flores-Calero es doctor en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de Madrid (España). Actualmente, es profesor titular en la Universidad de las Fuerzas Armadas ESPE (Ecuador). Durante su vida académica ha publicado sus trabajos científicos en revistas de prestigio internacional en las áreas de Inteligencia Artificial, Procesamiento Digital de Señales y Matemáticas Aplicadas.

References

U. R. Acharya, H. Fujita, V. K. Sudarshan, V. Sree, L. Wei, J. Eugene, D. N. Ghista, and R. S. Tan, “An Integrated Index for Detection of Sudden Cardiac Death Using Discrete Wavelet Transform and Nonlinear Features,” KNOWLEDGE-BASED SYSTEMS, 2015.

E. Ebrahimzadeh, A. Foroutan, M. Shams, R. Baradaran, L. Rajabion, M. Joulani, and F. Fayaz, “An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal,” Computer Methods and Programs in Biomedicine, vol. 169, pp. 19–36, 2019.

R. Devi, H. K. Tyagi, and D. Kumar, “A novel multi-class approach for early-stage prediction of sudden cardiac death,” Biocybernetics and Biomedical Engineering, vol. 39, no. 3, pp. 586–598, 2019.

A. Irshad, A. D. Bakhshi, and S. Bashir, “A Bayesian Filtering Application for T -wave Altemans Analysis,” 12th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad,

Pakistan, 13th - 17th January, pp. 222–227, 2015.

Q. Pham, K. J. Quan, and D. S. Rosenbaum, “T-Wave Alternans: Marker, Mechanism, and Methodology for Predicting Sudden Cardiac Death,” Journal of Electrocardiology, vol. 36, no. SUPPL., pp. 75–81, 2003.

J. J. Goldberger, M. E. Cain, S. H. Hohnloser, A. H. Kadish, B. P. Knight, M. S. Lauer, B. J. Maron, R. L. Page, R. S. Passman, D. Siscovick, W. G. Stevenson, and D. P. Zipes, “American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society Scientific Statement on Noninvasive Risk Stratification Techniques for Identifying Patients at Risk for Sudden Cardiac Death. A Scientific Statement From the American Heart Association Council on Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention,” Heart Rhythm, vol. 5, no. 10, 2008.

M. M. Demidova, A. Martín-Yebra, J. P. Martínez, V. Monasterio, S. Koul, J. Van Der Pals, D. Romero, P. Laguna, D. Erlinge, and P. G. Platonov, “T wave alternans in experimental myocardial infarction: Time course and predictive value for the assessment of myocardial damage,” Journal of Electrocardiology, vol. 46, no. 3, pp. 263–269, 2013.

H. Fujita, U. R. Acharya, V. K. Sudarshan, D. N. Ghista, S. V. Sree, L. W. J. Eugene, and J. E. Koh, “Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index,” Applied Soft Computing Journal, vol. 43, pp. 510–519, 2016.

J. Liu, G. Wu, C. Zhang, J. Ruan, D. Wang, M. Zhang, L. Wang, Y. Yang, X. Li, Y. Wang, R. Hui, Y. Zou, L. Kang, J. Wang, and L. Song, “Improvement in sudden cardiac death risk prediction by the enhanced American College of Cardiology/American Heart Association strategy in Chinese patients with hypertrophic cardiomyopathy,” Heart Rhythm, vol. 17, no. 10, pp. 1658–1663, 2020.

A. Parsi, D. O’Loughlin, M. Glavin, and E. Jones, “Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features,” IEEE Reviews in Biomedical Engineering, vol. 13, no. May 2020, pp. 5–16, 2020.

J. P. Martínez and S. Olmos, “Methodological principles of T wave alternans analysis: A unified framework,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 4, pp. 599–613, 2005.

T. W. Shen and Y. T. Tsao, “An improved spectral method of detecting and quantifying T-Wave Alternans for SCD risk evaluation,” Computers in Cardiology, vol. 35, pp. 609–612, 2008.

D. Lai, Y. Zhang, X. Zhang, Y. Su, and M. B. Bin Heyat, “An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers,” IEEE Access, vol. 7, pp. 94701–94716, 2019.

N. Betancourt, C. Almeida, and M. Flores-Calero, “T wave alternans analysis in ecg signal: A survey of the principal approaches,” in Information Technology and Systems (Á. Rocha, C. Ferrás, and M. Paredes, eds.), (Cham), pp. 417–426, Springer International Publishing, 2019.

F. J. Gimeno-Blanes, M. Blanco-Velasco, Ó. Barquero-Pérez, A. García-Alberola, and J. L. Rojo-álvarez, “Sudden cardiac risk stratification with electrocardiographic indices - A review on computational processing, technology transfer, and scientific evidence,” Frontiers in Physiology, vol. 7, no. MAR, pp. 1–17, 2016.

J. P. Amezquita-Sanchez, M. Valtierra-Rodriguez, H. Adeli, and C. A. Perez-Ramirez, “A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals,” Journal of Medical Systems, vol. 42, no. 10, 2018.

K. Rajbhandari Panday and D. R. Panday, “Heart Rate Variability (HRV),” Journal of Clinical & Experimental Cardiology, vol. 09, no. 04, 2018.

A. El-Menyar and N. Asaad, “T-wave alternans and sudden cardiac death,” Critical Pathways in Cardiology, vol. 7, no. 1, pp. 21–28, 2008.

R. L. Verrier, K. Kumar, and B. D. Nearing, “Basis for sudden cardiac death prediction by T-wave alternans from an integrative physiology perspective,” Heart Rhythm, vol. 6, no. 3, pp. 416–422, 2009.

B. D. Nearing and R. L. Verrier, “Modified moving average analysis of t-wave alternans to predict ventricular fibrillation with high accuracy,” Journal of applied physiology, vol. 92, no. 2, pp. 541–549, 2002.

D. Cuesta-Frau, P. Micó-Tormos, M. Aboy, M. O. Biagetti, D. Austin, and R. A. Quinteiro, “Enhanced modified moving average analysis of T-wave alternans using a curve matching method: A simulation study,” Medical and Biological Engineering and Computing, vol. 47, no. 3, pp. 323–331, 2009.

S. Bashir, A. D. Bakhshi, and M. A. Maud, “A template matched-filter based scheme for detection and estimation of t-wave alternans,” Biomedical Signal Processing and Control, vol. 13, no. 1, pp. 247–261, 2014.

R. L. Verrier, B. D. Nearing, M. T. L. Rovere, G. D. Pinna, M. A. Mittleman, J. T. Bigger, and P. J. Schwartz, “Ambulatory electrocardiogram-based tracking of t wave alternans in postmyocardial infarction patients

to assess risk of cardiac arrest or arrhythmic death,” Journal of cardiovascular electrophysiology, vol. 14, no. 7, pp. 705–711, 2003.

J. M. Smith, E. A. Clancy, C. R. Valeri, J. N. Ruskin, and R. J. Cohen, “Electrical alternans and cardiac electrical instability.,” Circulation, vol. 77, no. 1, pp. 110–121, 1988.

E. d. V. Garcia, “T-wave alternans: Reviewing the clinical performance, understanding limitations, characterizing methodologies,” Annals of Noninvasive Electrocardiology, vol. 13, no. 4, pp. 401–420, 2008.

B. Kitchenham, R. Pretorius, D. Budgen, O. Pearl Brereton, M. Turner, M. Niazi, and S. Linkman “Systematic literature reviews in software engineering a tertiary study,” Information and Software Technology, vol. 52, no. 8, pp. 792–805, 2010.

A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.

S. D. Greenwald, The development and analysis of a ventricular fibrillation detector. PhD thesis, Massachusetts Institute of Technology, 1986.

G. B. Moody, “The physionet / computers in cardiology challenge 2008: T-wave Alternans,” Computers in Cardiology, vol. 35, pp. 505–508, 2008.

V. Monasterio, G. D. Clifford, P. Laguna, and J. P. Martí Nez, “A multilead scheme based on periodic component analysis for T-Wave alternans analysis in the ECG,” Annals of Biomedical Engineering, vol. 38, no. 8, pp. 2532–2541, 2010.

S. Narayan, G. Botteron, and J. Smith, “T-wave alternans spectral magnitude is sensitive to electrocardiographic beat alignment strategy,” in Computers in Cardiology 1997, pp. 593–596, 1997.

M. AlMahamdy and H. B. Riley, “Performance study of different denoising methods for ECG signals", Procedia Computer Science, vol. 37, pp. 325–332, 2014.

U. Biswas, K. R. Hasan, B. Sana, and M. Maniruzzaman, “Denoising ECG signal using different wavelet families and comparison with other techniques,” 2nd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2015, no. May, pp. 21–23, 2015.

D. S. Rosenbaum, L. E. Jackson, J. M. Smith, H. Garan, J. N. Ruskin, and R. J. Cohen, “Electrical alternans and vulnerability to ventricular arrhythmias,” New England journal of medicine, vol. 330, no. 4, pp. 235-241, 1994.

Published

2022-08-02

How to Cite

Betancourt M., N. C., Almeida, C., & Flores-Calero, M. (2022). Heart Rate Variability and T Wave Alternans as risk stratification indices for detecting Sudden Cardiac Death: A Review. IEEE Latin America Transactions, 20(9), 2181–2188. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6488