Lifethreatening ventricular arrhythmia recognition by nonlinear descriptor
 Yan Sun^{1}Email author,
 Kap Luk Chan^{2} and
 Shankar Muthu Krishnan^{2}
https://doi.org/10.1186/1475925X46
© Sun et al; licensee BioMed Central Ltd. 2005
Received: 08 September 2004
Accepted: 24 January 2005
Published: 24 January 2005
Abstract
Background
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are ventricular cardiac arrhythmia that could be catastrophic and life threatening. Correct and timely detection of VT or VF can save lives.
Methods
In this paper, a multiscalebased nonlinear descriptor, the Hurst index, is proposed to characterize the ECG episode, so that VT and VF can be recognized as different from normal sinus rhythm (NSR) in the descriptor domain.
Results
This newly proposed technique was tested using MITBIH malignant ventricular arrhythmia database. The relationship between the ECG episode length and the corresponding recognition performance was studied. The experiments demonstrated good performance of the proposed descriptor. An accuracy rate as high as 100% was obtained for VT/VF to be recognized from NSR; for VT and VF to be recognized from each other, the recognition accuracy varies from 84.24% to 100%. In addition, the results were compared favorably against those obtained using Complexity measure.
Conclusions
There is strong potential for using the Hurst index for malignant ventricular arrhythmia recognition in clinical applications.
Keywords
Introduction
If a lifethreatening ventricular tachycardia (VT) or ventricular fibrillation (VF) is detected promptly, a high energy electrical shock can be delivered to the heart, in an attempt to return the heart to a normal sinus rhythm (NSR). If a normal sinus rhythm is misinterpreted as VT or VF, leading to delivering of an unnecessary shock, it can damage the heart, causing fatal consequences to the patient. Therefore, correct and prompt detection of VT or VF is of great importance. However, the detection of these lifethreatening cardiac arrhythmia is difficult because the waveform and frequency distribution of these lifethreatening arrhythmia changes with the prolonged duration [1]. Furthermore, practical problems such as poor contact, movement, interference, etc, can produce artifacts that mimic these rhythms [2].
Till now, many linear techniques for VT/VF detection have been developed, such as the probability density function method [3], rate and irregularity analysis [4], analysis of peaks in the shortterm autocorrelation function [5], sequential hypothesis testing algorithm [6, 7], correlation waveform analysis [8], four fast template matching algorithms [9], VFfilter method [2, 10], spectral analysis [1], and timefrequency analysis [11]. However, these methods exhibit disadvantages, some being too difficult to implement and compute for automated external defibrillators (AED's) and implantable cardioverter defibrillators (ICD's), and some only successful in limited cases. For example, the linear techniques [5, 11] using the features of amplitude or frequency have shown their limits, since the amplitude of ECG signal decreases as the VF duration increases, and the frequency distribution changes with prolonged VF duration. Therefore, more sophisticated signal processing techniques are needed to fully describe and characterize VT and VF and facilitate the development of new detection schemes with high correct detection rate, or equivalently, with low falsepositive and falsenegative performance statistics.
Recent studies [12, 13] have shown that the cardiac dynamics are complex and nonlinear. Even if they could be described by a set of differential equations, they would be of high dimensionality. Normally, each heart beat is initiated by a stimulus from pacemaker cells in the SA node in the right atrium. The activation wave then spreads through the atria to the AV junction. Following activation of the AV junction, the cardiac impulse spreads to the ventricular myocardium through a specialized network, the HisPurkinje system. This branching structure of the conduction system is a selfsimilar tree with finely scaled details on a microscopic level. The spread of the depolarization wave is represented by the QRS complex in ECG. Spectral analysis of the waveform reveals a broadband of frequencies. To explain the inverse powerlaw spectrum, West has conjectured that the repetitive branches of the HisPurkinje system represent a fractal set in which each generation of the selfsimilar tree imposes greater detail onto the system [14]. The effect of the finely branching fractal network is to subtly decorrelate the individual pulses that superpose to form the QRS complex. The distribution in path lengths resulting from the fractal nature of the branches give rise to a distribution of decorrelation time. Some methods developed based on the theory of nonlinear dynamics have been highlighted for the analysis of the signals generated from nonlinear system [15]. Due to the complex and nonlinear dynamical behavior of the cardiac conduction system, nonlinear dynamics or nonlinear mathematical models are considered to be suitable tools for the analysis of ECG signals. Nonlinear techniques have been proven to be major cornerstones for understanding the ECG signals [13, 16, 17].
Some nonlinear techniques [18–20] have been developed for lifethreatening ventricular arrhythmia recognition. However, there are still many problems requiring solution. The computational demands for most of the existing algorithms are considerably high and a long ECG episode duration is needed. In order to strike a balance between lower computational burden and reliable recognition performance, a nonlinear descriptor, the Hurst index, is proposed as a new tool in this study for recognition of the lifethreatening ventricular arrhythmia. The Hurst index is defined in the multiscale domain as a feature to quantify the nonlinear dynamical behavior (such as, selfsimilarity, roughness and irregularity) of the ECG signal for detecting the lifethreatening ventricular arrhythmia.
ECG episodes with VT and VF from MITBIH malignant arrhythmia database [21] are tested for cardiac abnormality recognition. The data also included some NSR signals to check on the validity of the algorithm. Experimental results are compared with those obtained by a typically used nonlinear technique, the Complexity measure, which has been shown to perform well for lifethreatening ventricular arrhythmia recognition [20]. In this paper, the complexity measure is Zheng's complexity measure without exception. Detailed description of Zheng's complexity measure technique can be find in [20].
The present paper is organized as follows. Mathematical background on the proposed nonlinear descriptor is given in Section. Methodology for the recognition of ventricular arrhythmia is described in Section. Section covers the experimental results and discussions. Lastly, a conclusion of the proposed study is given in Section.
Multiscalebased nonlinear descriptor
Multiscale analysis is a useful framework for many signal processing tasks. Wavelet transform is a good tool for multiscale analysis, which allows the expansion of a signal from the time domain into the timefrequency domain. In this paper, the Hurst index, defined in multiscale space, is proposed for the characterization of ECG episodes.
The Hurst index, H, is a single scalar parameter describing the fractal Brownian motion (fBm) model, which is a useful model for nonstationary stochastic selfsimilar processes with long term dependencies over wide ranges of frequencies [22]. fBm is an extension of the ordinary Brownian motion, and is a zeromean Gaussian nonstationary stochastic process B_{ H }(t), t ∈ ℝ, 0 <H < 1, [23]. Selfsimilarity is inherent to the fBm structure. The fractal dimension D is a commonly used parameter for measuring selfsimilarity. The relationship between the fractal dimension, D, and the Hurst index H is: D = S  H, where S is the topology dimension. For a onedimensional signal, S = 2; for a twodimensional image, S = 3 [24]. The fBm model has following features:

It is nonstationary, which necessitates some timedependent analysis.
E(B_{ H }(t)B_{ H }(s)) = σ^{2}/2(t^{2}H + s^{2}H  t  s^{2}H) (1)
where E(·) represents the expectation operator, σ is the standard deviation, t is a time variable, s is a time lag variable. Based on Equation (1), the variance of fBm, is computed as var(B_{ H }(t)) = σ^{2}t^{2H}.

It is selfsimilar, which necessitates some scaledependent analysis.
{B_{ H }(at)} ≜ a^{ H }B_{ H }(t), a ∈ ℝ^{+} (2)
where ℝ^{+} is the set of positive real numbers. ≜ means equality in distribution, which means that the fBm has stationary increments, and the probability properties of the process B_{ H }(t + s)  B_{ H }(t) only depend on the lag variable s. The scalar index H of fBm is related to the complexity and roughness of fBm samples.
Consider a discrete orthogonal wavelet decomposition of a given fBm, B_{ H }(t).
For any given resolution 2^{ J }, the wavelet meansquare representation of fBm is:
Computing the corresponding wavelet coefficients amounts to evaluating the following approximate coefficients a_{ j }[n] and detail coefficients d_{ j }[n]:
where φ(t) is the corresponding smooth function of wavelet ψ(t).
Flandrin et al. in [22] have deduced the following theorem: When normalized according to
Wavelet coefficients of fBm give rise to:
where V_{ ψ }(H) is constant, which depends on both the chosen wavelet and the fBm index H. It follows the powerlaw behavior of the wavelet coefficients' variance:
log_{2}(var(d_{ j }[n])) = (2H + 1)j + constant (9)
Therefore, the fBm index H (and hence the associated fractal dimension D = 2  H) can be easily obtained from the slope of this variance plotted as a function of scale in a loglog plot.
Lifethreatening ventricular arrhythmia recognition by Hurst index
For each testing ECG episode, the following steps are performed:

Perform wavelet decomposition and computation of its detail coefficients at different scales.

Compute the Hurst index H according to Equation (9).

Detect the lifethreatening ventricular arrhythmia in the feature space of H.
In this study, the wavelet used is a quadratic spline wavelet with compact support and one vanishing moment. It is a first derivative of a smooth function [25], whose discrete Fourier transform is:
The lowpass and highpass filters L(ω) and G(ω) are respectively:
The dyadic wavelet transform (WT) of a digital signal f(n) can be calculated with Mallat's algorithm [26] as follows:
Comparative Experimental Results and Discussions
Description of the test data
In this study, about 5076 ECG episodes are tested for performance evaluation of lifethreatening ventricular arrhythmia recognition using the proposed Hurst index. Among them, 2588 cases are NSR episodes, 1390 cases are VT episodes, and 1098 are VF episodes. In order to explore the effect of the time series lengths on the recognition performance using the proposed Hurst index, analyzing was conducted using different lengths of ECG episodes from 1 sec to 5.5 sec with a difference of 0.5 sec. For each length, the whole dataset was randomly divided into two equal parts for training and testing, respectively. From a clinical point of view, it is essential to recognize and diagnose malignant ventricular arrhythmia as soon as possible. This calls for detection with as short a length of the time series as possible.
Statistical results of Hurst index for episode characterization
Episode Length  Hurst index  

NSR  VT  VF  
Mean  SD  Mean  SD  Mean  SD  
1 sec  0.6099  0.0981  0.8117  0.0775  0.8567  0.0579 
1.5 sec  0.6206  0.0805  0.8269  0.0671  0.8597  0.0501 
2 sec  0.6317  0.0619  0.8373  0.0558  0.8618  0.0438 
2.5 sec  0.6349  0.0549  0.8398  0.0509  0.8682  0.0419 
3 sec  0.6389  0.0458  0.8445  0.0409  0.8766  0.0399 
3.5 sec  0.6389  0.0458  0.8445  0.0409  0.8766  0.0399 
4 sec  0.6395  0.0436  0.8452  0.0403  0.8794  0.0395 
4.5 sec  0.6398  0.04  0.8455  0.0397  0.8797  0.0392 
5 sec  0.6399  0.035  0.8458  0.0391  0.8799  0.0387 
5.5 sec  0.6399  0.035  0.8458  0.0388  0.8799  0.0386 
Statistical results of Hurst index for episode characterization
Episode Length  Complexity measure  

NSR  VT  VF  
Mean  SD  Mean  SD  Mean  SD  
1 sec  0.1674  0.0433  0.2775  0.0428  0.2798  0.0498 
1.5 sec  0.1476  0.0403  0.2562  0.0428  0.2601  0.0498 
2 sec  0.1319  0.037  0.2413  0.0335  0.2454  0.0432 
2.5 sec  0.1245  0.0366  0.2311  0.0335  0.239  0.0432 
3 sec  0.1192  0.0363  0.2229  0.0349  0.2351  0.037 
3.5 sec  0.1129  0.0348  0.2168  0.0349  0.2298  0.037 
4 sec  0.1095  0.0332  0.2149  0.0342  0.2242  0.0343 
4.5 sec  0.1071  0.0321  0.2136  0.0342  0.2205  0.0343 
5 sec  0.1056  0.0315  0.2129  0.0342  0.2187  0.0341 
5.5 sec  0.1056  0.0313  0.2129  0.0339  0.2187  0.0341 
From the results shown in Figure 4 and 5, the following observation can be made.

As the episode length increases, the mean of Hurst index for every type of rhythm basically increases and tends to approach a relatively stable value, while the standard deviation decreases gradually.

For a particular episode length, from NSR to VT then to VF, the corresponding Hurst index increases gradually. The increase from NSR to VT is more than the increase from VT to VF.

As the episode length increases, the mean of Complexity measure for every type of rhythm basically decreases and tends to approach a relatively stable value, while the standard deviation decreases gradually.

For a particular episode length, from NSR to VT then to VF, both the Hurst index and the Complexity measure increase gradually, in which, the increase from NSR to VF is far more than the increase from VT to VF.

The mean values of Hurst index vary slower than those of Complexity measure as the episode length increases from 1 sec to 5.5 sec. It is concluded that the Hurst index is more stable than the Complexity measure with respect to episode lengths.
Using the Hurst index for VT or VF recognition from NSR with different episode lengths, there is no false detection, meaning that the VT/VF can be totally correctly recognized from NSR without exception. For the Complexity measure, when the length of ECG episode is longer than 1 sec, it has as good performance as the Hurst index; when the length of the ECG episode is 1 sec, there is 6 false negatives and 27 false positives; when the length of the ECG episode is 1.5 sec, there is 1 false negatives and 5 false positives. The statistical values of SE, SP and ACR for VT/VF recognition from NSR using the Hurst index are all 100%. Hence, the Hurst index can be used to detect VT and VT earlier.
Statistical values of SE, SP and ACR for VF differentiation from VT
Episode Length  Hurst index  Complexity measure  

SE  SP  ACR  SE  SP  ACR  
1 sec  0.8351  0.8482  0.8424  0.8242  0.8302  0.8275 
1.5 sec  0.8780  0.8698  0.8734  0.8689  0.8597  0.8637 
2 sec  0.9080  0.8834  0.8942  0.9007  0.8798  0.8890 
2.5 sec  0.9408  0.9158  0.9268  0.9381  0.9194  0.9277 
3 sec  0.9608  0.9439  0.9513  0.9654  0.9489  0.9562 
3.5 sec  0.9754  0.9669  0.9707  0.9818  0.9734  0.9771 
4 sec  0.9854  0.9849  0.9851  0.9918  0.9885  0.9899 
4.5 sec  0.9936  0.9914  0.9924  1  0.9986  0.9992 
5 sec  1  0.9978  0.9988  1  1  1 
5.5 sec  1  1  1  1  1  1 
Computation time comparison in seconds
Length of episode  Hurst index  Complexity measure  Length of episode  Hurst index  Complexity measure 

1 sec  0.0546  0.0654  1.5 sec  0.0697  0.0824 
2 sec  0.0794  0.1143  2.5 sec  0.0933  0.1538 
3 sec  0.1168  0.2176  3.5 sec  0.1401  0.2991 
4 sec  0.1885  0.4003  4.5 sec  0.2407  0.609 
5 sec  0.2803  0.6833  5.5 sec  0.3122  0.7792 

The performance on differentiating VT and VF is worse than the performance of VT/VF recognition from NSR, for both the Hurst index and the Complexity measure.

The recognition performance by either descriptors improves as the length of ECG episode increases.

When the length of ECG episode is less than or equal to 2 sec, the recognition performance for the Hurst index is better. When the length of ECG episode is longer than 2 sec and less than 5 sec, the recognition performance for the Complexity measure is better. When the length of ECG episode is longer than 5 sec, VT and VF can be 100% differentiated with either descriptor, the recognition performance for both descriptors are same.
According to Table 4, the computational time for the Hurst index is less than that for the Complexity measure. These two algorithms are programmed using MATLAB 5.3 running on a SUN SPARC333MHz workstation. The computational burden for the Hurst index is O(N log_{2} N), while the computational burden for the complexity is O(N^{2}), where N is the length of ECG episode. It is noted that with more powerful computer programming in C, the computational speed will be further improved.
Time is an important factor for saving lives in clinical situations, therefore, algorithm with less computational burden is obviously preferred. In addition, using short ECG episode length is preferred for earlier detection of arrhythmia (such as VT/VF). Based on the experimental results, it is observed that the Hurst index has a better potential for clinical adaptation than the Complexity measure.
Conclusions
In this paper, a new technique based on multiscale analysis and nonlinear dynamics was presented for VT and VF recognition. Hurst index defined across multiscale was proposed for characterizing ECG episode so that lifethreatening arrhythmia can be recognized. Furthermore, upon applying to the MITBIH malignant ventricular arrhythmia database, the performance for malignant arrhythmia recognition using Hurst index was compared with that using Zheng's complexity measure. The Hurst index requires less computation and is more reliable in detecting VT and VF with short ECG episode. There is strong potential for using the Hurst index for malignant ventricular arrhythmia recognition in clinical applications.
Declarations
Acknowledgments
The authors wish to extend their sincere appreciation to Nanyang Technological University, Singapore for supporting the present work. The authors also acknowledge the clinical collaboration from Singapore General Hospital.
Authors’ Affiliations
References
 Barro S, Ruiz R, Cabello D, Mira J: Algorithmic sequential decisionmaking in the frequency domain for life threatening ventricular arrhythmias and imitative artifacts: a diagnostic system. J Biomed Eng 1989, 11(4):320–328.View ArticleGoogle Scholar
 Clayton RH, Murray A, Campbell RW: Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG. Med Biol Eng Comput 1993, 31: 111–117.View ArticleGoogle Scholar
 Langer A, Heilman MS, Mower MM: Considerations in the development of the automatic implantable defibrillator. Medical Instrumentation 1976, 10(3):163–167.Google Scholar
 Ripley KL, Bump TE, Arzbaecher RC: Evaluation of techniques for recognition of ventricular arrhythmias by implanted devices. IEEE Transactions on Biomedical Engineering 1989, 36(6):618–624. 10.1109/10.29456View ArticleGoogle Scholar
 Chen S, Thakor NV, Mover MM: Ventricular fibrillation detection by a regression test on the autocorrelation function. Med Biol Eng Comput 1987, 25(3):241–249.View ArticleGoogle Scholar
 Thakor NV, Natarajan A, Tomselli G: Multiway sequential hypothesis testing for tachyarrhythmia discrimination. IEEE Transactions on Biomedical Engineering 1994, 41(5):480–487. 10.1109/10.293223View ArticleGoogle Scholar
 Chen SW, Clarkson PW, Fan Q: A robust detection algorithm for cardiac arrhythmia classification. IEEE Transactions on Biomedical Engineering 1996, 43: 1120–1125. 10.1109/10.541254View ArticleGoogle Scholar
 Lin D, Jenkins JM, DiCarlo LA, MacDonald RS: Arrhythmia diagnosis using morphology and timing from atrial and ventricular leads. Computers in Cardiology 1988, 159–162. (September)Google Scholar
 Throne RD, Jenkins JM, DiCarlo LA: A comparison of four new timedomain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology. IEEE Transactions on Biomedical Engineering 1991, 38(5):561–570. 10.1109/10.81581View ArticleGoogle Scholar
 Kuo S, Dillman R: Computer detection of ventricular fibrillation. Comput Cardiol 1978, 347–349.Google Scholar
 Afonso VX, Tompkoins WJ: Detecting ventricular fibrillation: selecting the appropriate timefrequency analysis tool for the application. IEEE Engineering in Medicine and Biology 1995, 152–159. 10.1109/51.376752Google Scholar
 Dirk H, Bernd P, Hanspeter H, Ulrich Z: Nonlinear coordination of cardiovascular automatic control. IEEE Engineering in Medicine and Biology 1998, 17(6):17–21. 10.1109/51.731315View ArticleGoogle Scholar
 Seidel H, Herzel H: Investigating the dynamics of atrioventricular delay. IEEE Engineering in Medicine and Biology 1998, 17(6):22–25. 10.1109/51.731316View ArticleGoogle Scholar
 West BJ: Fractal Physiology and Chaos in Medicine. World Scientific, Singapore 1990.Google Scholar
 Abarbanel HI: Analysis of Observed Chaotic Data. Springer Verlag, Berlin; 1996.View ArticleGoogle Scholar
 Fojt O, Holcik J: Applying nonlinear dynamics to ECG signal processing. IEEE Engineering in Medicine and Biology 1998, 17(2):96–101. 10.1109/51.664037View ArticleGoogle Scholar
 Cohe ME, Hudson DL, Deedwania PC: Applying continuous chaotic modeling to cardiac signal analysis. IEEE Engineering in Medicine and Biology 1996, 15(5):97–102. 10.1109/51.537065View ArticleGoogle Scholar
 Ravelli F, Antolini R: Complex dynamics underlying the human electrocardiogram. Biol Cybern 1992, 67: 57–65. 10.1007/BF00201802View ArticleGoogle Scholar
 Jenkins JM, Caswell SA: Detection algorithms in implantable cardioverter defibrillators. Proc IEEE 1996, 84: 428–445. 10.1109/5.486745View ArticleGoogle Scholar
 Zheng XS, Zhu YS, Thakor NV, Wang ZZ: Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Transactions on Biomedical Engineering 1999, 46(5):548–555. 10.1109/10.759055View ArticleGoogle Scholar
 MITBIH arrhythmia database[http://www.physionet.org/physiobank/database/mitdb/]
 Flandrin P: Wavelet analysis and synthesis of fractal Brownian motion. IEEE Transactions Information Theory 1992, 38(2):910–917. 10.1109/18.119751MathSciNetView ArticleGoogle Scholar
 Mandelbrot BB, VanNess JW: Fractal Brownian motions, fractional noises and applications. SIAM review 1968, 10(4):422–437.MathSciNetView ArticleGoogle Scholar
 Falconer K: Fractal Geometry. J Wiley and Sons, New York; 1990.Google Scholar
 Mallat S: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992, 14(7):710–732. 10.1109/34.142909MathSciNetView ArticleGoogle Scholar
 Mallat S: Zerocrossing of a wavelet transform. IEEE Transactions on Information Theory 1991, 37(4):1019–1033. 10.1109/18.86995MathSciNetView ArticleGoogle Scholar
 Li C, Zheng C, Tai CF: Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering 1995, 42(1):21–29. 10.1109/10.362922View ArticleGoogle Scholar
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