Computer Software tool for heart rate variability (HRV), T-wave alternans (TWA) and heart rate turbulence (HRT) analysis from ECGs

Summary Background This paper presents a software package for quantitative evaluation of heart rate variability (HRV), heart rate turbulence (HRT), and T-wave alternans (TWA) from ECG recordings. The software has been developed for the purpose of scientific research rather than clinical diagnosis. Material/Methods The software is written in Matlab Mathematical Language. Procedures for evaluation of HRV, HRT and TWA were implemented. HRV analysis was carried out by applying statistical and spectral parametric and nonparametric methods. HRT parameters were derived using the Schmidt algorithm. TWA analysis was performed both in spectral and in time domain by applying Poincare mapping. A flexibility of choosing from a number of classical modelling approaches and their modifications was foreseen and implemented. The software underwent preliminary verification tests both on ECGs from the Physionet online ECG signal repository and recordings taken at the Department of Electrocardiology of the Medical University Hospital in Lodz. Results The result of the research is a program enabling simultaneous analysis of a number of parameters computed from ECG recordings with the use of the indicated analysis methods. The program offers options to preview the intermediate results and to alter the preprocessing steps. Conclusions By offering the possibility to cross-validate the results of analyses obtained by several methods and to preview the intermediate analysis steps, the program can serve as a helpful aid for clinicians in comprehensive research studies. The software tool can also be utilized in training programs for students and medical personnel.

[1]  S. Hohnloser,et al.  Risk Stratification Using T‐Wave Alternans: More Questions Waiting to be Answered , 2008, Journal of cardiovascular electrophysiology.

[2]  W. M. Carey,et al.  Digital spectral analysis: with applications , 1986 .

[3]  Moacir Fernandes de Godoy,et al.  Preoperative nonlinear behavior in heart rate variability predicts morbidity and mortality after coronary artery bypass graft surgery. , 2009, Medical science monitor : international medical journal of experimental and clinical research.

[4]  Federico Lombardi,et al.  Heart rate turbulence: standards of measurement, physiological interpretation, and clinical use: International Society for Holter and Noninvasive Electrophysiology Consensus. , 2008, Journal of the American College of Cardiology.

[5]  J Ruta,et al.  Usefulness of the Poincaré maps in detection of T-wave alternans in precordial leads of standard ECG--a comparison with the spectral method. , 2001, Medical science monitor : international medical journal of experimental and clinical research.

[6]  M. Dudziak,et al.  Assessment of a single monomorphic ventricular ectopy from the right ventricular outflow tract in standard and high resolution electrocardiogram , 2010, Archives of medical science : AMS.

[7]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[8]  A L Goldberger,et al.  The pNNx files: re-examining a widely used heart rate variability measure , 2002, Heart.

[9]  A. Camm,et al.  Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction , 1999, The Lancet.

[10]  Piet M. T. Broersen,et al.  Automatic spectral analysis with time series models , 2002, IEEE Trans. Instrum. Meas..

[11]  Fabio Badilini,et al.  Cubic Spline Baseline Estimation In Ambulatory ECg Recordings For The Measurement Of ST Segment Displacements , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[12]  H N Keiser,et al.  Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. , 1977, Computers and biomedical research, an international journal.

[13]  Barry J Maron,et al.  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 Cardi , 2008, Journal of the American College of Cardiology.

[14]  R. Verrier,et al.  Noninvasive Sudden Death Risk Stratification by Ambulatory ECG‐Based T‐Wave Alternans Analysis: Evidence and Methodological Guidelines , 2005, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[15]  Leandro Rodríguez Liñares,et al.  An open source tool for heart rate variability spectral analysis , 2011, Comput. Methods Programs Biomed..

[16]  P. Strumiłło,et al.  Poincare mapping for detecting abnormal dynamics of cardiac repolarization , 2002, IEEE Engineering in Medicine and Biology Magazine.

[17]  Juan Pablo Martínez,et al.  Methodological principles of T wave alternans analysis: a unified framework , 2005, IEEE Transactions on Biomedical Engineering.

[18]  Mika P. Tarvainen,et al.  Kubios HRV — A Software for Advanced Heart Rate Variability Analysis , 2009 .

[19]  D. J. Bartholomew,et al.  Time Series Analysis Forecasting and Control , 1971 .

[20]  David Atienza Alonso ECG baseline wander removal and noise suppression analysis in an embedded platform , 2009 .

[21]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[22]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[23]  G.D. Clifford,et al.  An open-source standard T-Wave alternans detector for benchmarking , 2008, 2008 Computers in Cardiology.