A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats

The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control. We have developed a novel point-process-based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the PhysioNet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device.

[1]  N Lippman,et al.  Comparison of methods for removal of ectopy in measurement of heart rate variability. , 1994, The American journal of physiology.

[2]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[3]  R. Cohen,et al.  An Efficient Algorithm for Spectral Analysis of Heart Rate Variability , 1986, IEEE Transactions on Biomedical Engineering.

[4]  Pablo Laguna,et al.  An efficient method for handling ectopic beats using the heart timing signal , 2006, IEEE Transactions on Biomedical Engineering.

[5]  Emery N. Brown,et al.  Analysis of heartbeat dynamics by point process adaptive filtering , 2006, IEEE Transactions on Biomedical Engineering.

[6]  Pablo Laguna,et al.  Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal , 2003, IEEE Transactions on Biomedical Engineering.

[7]  R W de Boer,et al.  Description of heart-rate variability data in accordance with a physiological model for the genesis of heartbeats. , 1985, Psychophysiology.

[8]  D Sapoznikov,et al.  Computer processing of artifact and arrhythmias in heart rate variability analysis. , 1992, Computer methods and programs in biomedicine.

[9]  M N Cheung,et al.  Detection of and recovery from errors in cardiac interbeat intervals. , 1981, Psychophysiology.

[10]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[11]  Emery N. Brown,et al.  Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics , 2009, IEEE Transactions on Biomedical Engineering.

[12]  J. Leroy Folks,et al.  The Inverse Gaussian Distribution: Theory: Methodology, and Applications , 1988 .

[13]  E.N. Brown,et al.  Correction of Erroneous and Ectopic Beats Using a Point Process Adaptive Algorithm , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Guohua Pan,et al.  Local Regression and Likelihood , 1999, Technometrics.

[15]  W. Marsden.  I and J , 2012 .

[16]  Kameshwar Poolla,et al.  Threshold modeling of autonomic control of heart rate variability , 2000, IEEE Transactions on Biomedical Engineering.

[17]  George B. Moody,et al.  Spectral analysis of heart rate without resampling , 1993, Proceedings of Computers in Cardiology Conference.

[18]  U. RajendraAcharya.  Advances in cardiac signal processing , 2007 .

[19]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[20]  M. Wand.  Local Regression and Likelihood , 2001 .

[21]  Jason D. Moss,et al.  Real-Time Correction of Heart Interbeat Intervals , 2007, IEEE Transactions on Biomedical Engineering.

[22]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

[23]  Luca Citi,et al.  A point process local likelihood algorithm for robust and automated heart beat detection and correction , 2011, 2011 Computing in Cardiology.

[24]  M. W. van der Molen,et al.  A nonstationarity test for the spectral analysis of physiological time series with an application to respiratory sinus arrhythmia. , 1992, Psychophysiology.

[25]  黄亚明.  PhysioBank , 2009 .

[26]  Sabine Van Huffel,et al.  Accurate R peak detection and advanced preprocessing of normal ECG for heart rate variability analysis , 2010, 2010 Computing in Cardiology.

[27]  B. Mandelbrot,et al.  RANDOM WALK MODELS FOR THE SPIKE ACTIVITY OF A SINGLE NEURON. , 1964, Biophysical journal.

[28]  M. Malik,et al.  Prognostic value of heart rate variability after myocardial infarction. A comparison of different data-processing methods , 2006, Medical and Biological Engineering and Computing.

[29]  G. Berntson,et al.  An approach to artifact identification: application to heart period data. , 1990, Psychophysiology.

[30]  E. Brown,et al.  A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. , 2005, American journal of physiology. Heart and circulatory physiology.

[31]  Emery N. Brown,et al.  Characterizing nonlinear heartbeat dynamics within a point process framework , 2010, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Niels Keiding,et al.  Statistical Models Based on Counting Processes , 1993 .

[33]  Riccardo Barbieri,et al.  Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework , 2008, IEEE Transactions on Biomedical Engineering.

[34]  Pablo Laguna,et al.  Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model , 2000, IEEE Transactions on Biomedical Engineering.

[35]  C. Peng,et al.  Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. , 1996, The American journal of physiology.

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

[37]  Marimuthu Palaniswami,et al.  A new model-based ectopic beat correction algorithm for heart rate variability , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  L. Tarassenko,et al.  Characterizing artefact in the normal human 24-hour RR time series to aid identification and artificial replication of circadian variations in human beat to beat heart rate using a simple threshold , 2002, Computers in Cardiology.

[39]  Jianfeng Feng,et al.  Computational neuroscience , 1986, Behavioral and Brain Sciences.

[40]  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 .