Research Article  Open Access
Xiangkui Wan, Kanghui Yan, Linlin Zhang, Yanjun Zeng, "A TimeDomain Hybrid Analysis Method for Detecting and Quantifying TWave Alternans", Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 502981, 10 pages, 2014. https://doi.org/10.1155/2014/502981
A TimeDomain Hybrid Analysis Method for Detecting and Quantifying TWave Alternans
Abstract
Twave alternans (TWA) in surface electrocardiograph (ECG) signals has been recognized as a marker of cardiac electrical instability and is hypothesized to be associated with increased risk for ventricular arrhythmias among patients. A novel timedomain TWA hybrid analysis method (HAM) utilizing the correlation method and least squares regression technique is described in this paper. Simulated ECGs containing artificial TWA (cases of absence of TWA and presence of stationary or timevarying or phasereversal TWA) under different baseline wanderings are used to test the method, and the results show that HAM has a better ability of quantifying TWA amplitude compared with the correlation method (CM) and adapting match filter method (AMFM). The HAM is subsequently used to analyze the clinical ECGs, and results produced by the HAM have, in general, demonstrated consistency with those produced by the CM and the AMFM, while the quantifying TWA amplitudes by the HAM are universally higher than those by the other two methods.
1. Introduction
The Twave alternans (TWA) has been considered as one of the most promising markers of sudden cardiac death (SCD) over the past 10 years. TWA is a phenomenon appearing in the surface electrocardiograph (ECG) as a consistent fluctuation in the repolarization morphology on an “everyotherbeat” basis (2 : 1 behavior). This fluctuation refers to a beattobeat variability in the amplitude, morphology, and/or polarity of the Twave. Numerous clinical studies have demonstrated that TWA is associated with ventricular arrhythmias. Nowadays TWA has been considered an independent predictor of cardiac arrhythmias.
Several signal processing methods have been proposed to detect and estimate TWA in the ECG on a singlelead or multilead basis [1–8]. And a comprehensive and systematic discussion of methods for TWA detection and analysis is reported in [9]. Most widely used TWA detection methods work in two different domains: time and frequency.
The disadvantage of the frequency based methods is that they treat the alternans signal as a stationary wave with the constant amplitude and phase, which is not true in general. They cannot detect nonstationary characteristics of the signal.
The timedomain methods can detect the TWA of nonstationary ECG signal in short time, and they have also been used on Holter data. The correlation method (CM) [6, 7], as a wellknown timedomain method, performs well under different conditions, but it is sensitive to noise, especially to baseline wandering. In the presence of baseline oscillations at TWA frequency, a strong overestimation of TWA mean amplitude, and even TWA detection from TWAfree ECG tracings, is produced by the CM. And in the presence of higher frequency baseline fluctuations, the CM is not able to identify TWA [10]. An adapting match filter method (AMFM) was proposed by the same authors of the CM to overcome the CM limitations [11]. The AMFM yielded a significant improvement in the algorithmbased identification of duration and amplitude of TWA from ECG tracings with frequency of baseline oscillations both lower and higher than that of TWA. Nevertheless, in the presence of baseline fluctuations at the TWA frequency, it produced erroneous TWA detection from ECG tracing with no TWA and even strong overestimation of TWA amplitude, when present.
Based on above background, we propose a hybrid approach for the TWA detection, which is based on the correlation method and the least squares regression technique. The study aims to develop a novel TWA detector to overcome the CM limitations, which can detect and measure transient TWA with more accuracy in the time domain, even in the presence of higher frequency baseline fluctuations.
The rest of the paper is organized as follows. In Section 2, we present a novel method of TWA detection; simulated cases and clinical cases are also prepared. Then, in Section 3, we report the results of its validation on the simulation database and clinical database and compare the results to that of the CM and the AMFM. Next, in Section 4, we give the discussion. Finally, we summarize the conclusions of this work in Section 5.
2. Material and Methods
2.1. The Hybrid Analysis Method (HAM) Using Correlation Method and Least Squares Regression Technique
The hybrid analysis method consists of three different blocks: preprocessing, TWA detection, and TWA evaluation. The whole TWA analysis process is described as follows.
2.1.1. Data Preprocess
Before detecting TWA, the clinical ECG used here are required to be submitted to a preliminary preprocessing stage. This consists of various steps, which are baseline wandering suppression, QRS complex detection, and segmentation of the Twave.(i)Baseline wandering suppression: this is performed using a cubic spline interpolation technique [12].(ii)QRS complex detection: it is determined using a waveletbased algorithm [13].(iii)Twave segmentation: it is done by selecting intervals of 300 ms, beginning at a distance from the QRS fiducial point dependent on the interval. The interval onset for the th beat, , is given by the expression (iv)Twave alignment: after Twave segmentation, 128 consecutive Twaves present in the ECG are used to compute the median Twave (, which has each sample point given by the median value of the corresponding sample points of the 128 available Twaves), which is used as a template. Synchronization of the th Twave is performed according to a recursive procedure that keeps the segmented Twave window length constant but varies its position ±30 ms from the original position, with a time increment of one sample point. For each position of the Twave window, the windowed th Twave is crosscorrelated against the template. Optimal alignment occurs when maximum correlation is reached.
2.1.2. Qualitative Detection of TWA
After data preprocessing, TWA is detected by looking for an alternating trend in the Twave morphology quantified by a correlation index. To this aim, an alternans correlation index (ACI) is computed to measure morphological changes of each of the consecutive waves in comparison to [12], which is as shown as follows: where is the median Twave computed using 128 Twaves available in each ECG tracing. is the number of samples in each Twave.
is defined as the ratio of the maximum value of the crosscorrelation function of and over the maximum value of the autocorrelation function of . is classified as alternating.
The presence of TWA is considered when the value of ACI strictly oscillates (not necessary around one) in the case of monophasic TWA for at least 7 consecutive beats. Figure 1 shows an example of alternating values of , indicating the presence of TWA.
To limit false detections caused by noise, a local threshold criterion, with equal to 0.06 [6], is considered, such that ACI values alternations have to exceed 0.12 for at least seven consecutive beats to be detected as TWA.
2.1.3. Quantitative Estimation of TWA
The odd and even beats of the above detected consecutive beats are labeled as and , respectively. The odd Twaves are obtained from series and the even Twaves are obtained from series. The odd Twaves constitute a matrix: where is the th point of the th odd Twave. Analogously the even Twave matrix can be constituted.
The amplitude corrections of odd and even Twaves are performed using the firstdegree polynomial as shown below: where is the th row and th column point of odd (or even) Twave matrix. And the coefficients , are estimated by the linear least squares fitting process.
Each column vector of is divided into 7point epochs, and (4) is recursively applied to each epoch throughout the entire . Denote as the th deviation point of from the fitting line: Then the mean deviation value of can be expressed as follows:
If , then the is considered to be corrected and replaced by the th column mean value (as shown in (7)) of odd (or even) Twave matrix. Consider And the amplitude correction of the entire is recalculated, until the or .
A specific example of amplitude correction of odd Twave matrix using the linear fitting function is shown in Figure 2. Figure 2(a) represents the uncorrected Twaves and Figure 2(b) represents the corrected Twaves.
The corrected matrixes for odd and even Twaves are known as and , respectively. Measure as the maximum absolute value of the difference between and : where denotes the local TWA (i.e., relative to a single odd (or even) beat), .
The TWA of the analyzed consecutive ECG segment (segment TWA) is measured as the mean value of measured local TWAs: And the global TWA (i.e., relative to the entire ECG tracing analyzed) is measured as the mean value of segment TWAs:
The above process can be described as the block diagram (Figure 3).
2.2. Simulated Cases
There is no generally accepted TWAmeasuring criterion to be used as a goldstandard. Therefore, a simulation approach was used in the present study in different controlled cases.
A realistic, clean simulated ECG was obtained as a Kfold repetition of a single beat extracted from a real ECG [14]. This guarantees that all the Twaves of the simulated ECG are identical, so no TWA can be present in the original signal. In particular, we used a 0.7 s beat sampled at 500 samples per second. The length of each simulated ECG tracing was assumed to count 128 consecutive heart beats. Our choice relies on the fact that 128 consecutive beats were originally used for SM applications, although some timedomain methods (e.g., modified moving average) use shorter ECGs [15]. A constant interval of 0.7 s was assumed, so that TWA fundamental frequency was 0.71 Hz (i.e., or 0.5 cycles per beat). TWA was simulated by varying Twave amplitude (10, 50, and 100 ) in a time window of 160 ms centered around the Twave apex.
Four different sets of ECG simulation were considered, respectively, reproducing the cases relative to the absence of TWA, the presence of stationary TWA, the presence of timevarying TWA, and the phasereversal TWA, which are described below.
2.2.1. Case 1: Simulated ECG Tracing with No TWA
The simulated ECG tracing with no TWA (N_TWA) is assumed not to be affected by any kind of noise. This simulated signal is thought to test the ability of recognizing the absence of TWA, which is represented in Figure 4(a).
(a)
(b)
2.2.2. Case 2: Simulated ECG Tracings with Stationary TWA
The simulated ECG tracings with stationary TWA (S_TWA) are designed to test the ability of quantifying TWA amplitude in the presence of stationary alternating Twave profiles. Three kinds of simulated ECG tracings were considered, namely, a tracing with a 10 TWA (S_TWA10), a tracing with a 50 TWA (S_TWA50), and a tracing with a 100 TWA (S_TWA100). An example of a tracing with a 50 TWA is represented in Figure 3(b).
2.2.3. Case 3: Simulated ECG Tracings with TimeVarying TWA
ECG with visible TWA clearly shows the nonstationary nature of this phenomenon, whose variability often shows onoff or cyclic trends. Evaluation of dynamic aspects of TWA is important in clinics since transient TWA has been observed during acute ischemia [16]. To test the ability of the HAM in detecting nonstationary TWA, two simulated ECG tracings were considered, each one incorporating a specific beattobeat varying (and then, timevarying) sequence. Sinusoidal sequences, with 128 beats period were affecting the first (TV_TWA1) ECG tracing, while An varying from 50 to 20 , following a smoothed (24 beats transition) step pattern, was affecting the second ECG tracing (cascaded TWA, TV_TWA2). The two simulated tracings were characterized by a uniform profile of TWA, which are represented in Figures 5(a) and 5(b). The examples of TV_TWA1 and TV_TWA2 are represented in Figures 6(a) and 6(b), respectively.
(a)
(b)
(c)
(a)
(b)
(c)
2.2.4. Case 4: Simulated ECG Tracing with PhaseReversal TWA
Arrhythmias can sometimes trigger a phasereversal so that the alternans pattern changes from ABABAB to BABABA [5]. The simulated ECG tracing with phasereversal TWA (PR_TWA) is designed to test the ability of the method in detecting phasereversal TWA. PR_TWA tracing incorporates a stationary TWA, which changes phase twice, at beats 40 and 80, respectively. This simulated case may also be used to help in the interpretation of realistic cases in which a beat is missed (false negative QRS detection) or wrongly inserted (false positive QRS detection). An example is represented in Figure 6(c).
Finally the noise is also considered to be added to above simulated ECG tracings in this study. In clinical settings, power line interference is generally eliminated by a hardware filter. When computing the ACI indexes (2) the white noise is already taken into account. So baseline wandering is considered in the present simulated cases which might cause erroneous detection of TWA. Baseline wandering can be eliminated through the preprocessing state, but elimination related to T wave variability should be prevented because TWA is a specific case of it [4]. Based on these considerations, ECG simulations with baseline wandering are considered. Baseline wanderings are simulated with a sinusoid of 0.1 mV amplitude and various frequencies: 0.30, 0.71, and 1.50 Hz, respectively, which we denote as bw030, bw071, and bw150. These frequencies are, respectively, lower, equal, and greater than TWA frequency. The frequency of 0.30 Hz relates to a usual breathing pattern in patients. And the baseline fluctuations are simply added to each simulated ECG tracing. Two representative examples of our simulated ECG tracings, with and without baseline fluctuations, are displayed in Figure 7.
(a)
(b)
2.3. Clinical Cases
Two clinical data sets are considered in this study: ECG tracings from healthy subjects (Hsubjects) and that from patients.
ECG tracings from Hsubjects belong to the Digital Electrocardiology Study Databases of Liuhuaqiao Hospital, Guangzhou, which include 320 Holter ECG tracings from Hsubjects. The study was approved by the institutional research ethics committee of Guangzhou Medical College, and it was conducted following the required rules for human subjects’ research principles, according to the Declaration of Helsinki, as well as to Title 45, U.S. Code of Federal Regulations, Part 46, Protection of Human Subjects, Revised November 13, 2001, effective December 13, 2001. Each subject underwent 10min ECG recording in resting conditions. Nine standard leads (V1–V6, I, II, and III) were recorded using equipment by SiemensElema AB and digitized at a sampling rate of 500 Hz with amplitude resolution of . Leads aVF, aVR, and aVL were derived from leads I, II, and III.
ECG tracings from patients belong to the TWave Alternans Challenge Database (TWACD) [17], which contains 100 multichannel ECG records sampled at 500 Hz with 16 bit resolution over a ±32 mV range. The subjects include patients with myocardial infarctions, transient ischemia, ventricular tachyarrhythmia, and other risk factors for sudden cardiac death, as well as healthy controls and synthetic cases with calibrated amounts of Twave alternans. The databases are chosen for two reasons: one is that previous studies found Twave alternans episodes, some of them related to annotated ischemic episodes. Another is that the databases are wellknown and available by many research groups.
In the specific, a group of fourteen healthy subjects was compared with a group of fourteen patients. A subject was classified as belonging to the Hgroup when fulfilling the following criteria [18]:(1)no overt cardiovascular disease or history of cardiovascular disorders (including stroke, TIA, and peripheral vascular disease);(2)no history of high blood pressure (>150/90 mmHg);(3)not taking medication;(4)no other chronic illness (e.g., diabetes, asthma, chronic obstructive pulmonary disease, etc.);(5)diagnosed as being healthy if evaluated by a physician for cardiovascularrelated syndrome (chest pain, palpitation, syncope);(6)normal physical examination;(7)sinus rhythm in 12lead ECG without any suspicious abnormalities (e.g., signs of ventricular hypertrophy, inverted Twave, intraventricular conduction disturbances);(8)normal echo and normal ECG exercise testing in the presence of suspicious ECG changes;(9)no pregnancy.
2.4. Statistics
To evaluate the ability of the presented method to quantify TWA, the other two related timedomain methods, which are the CM and the AMFM, are used here for comparison.
In our simulation study, the root mean square error () in the estimate of TWA amplitudes is computed [13]: where is the total number of beats in an ECG tracing, (relative to the th beat) is assumed equal to the absolute value of the maximum difference between the th and the th Twave sample amplitude, and is the estimated local TWA (relative to the th beat) by the three competing methods. Subscript is for either the HAM or the CM or the AMFM. In this study, the resolution of is , and the predefined are considered as constitutive reference TWAamplitude signals (goldstandard).
When analyzing clinical data, the Lilliefors test was used to evaluate the hypothesis that estimated TWA had a normal distribution (significance was set at 5% level) over a population. Comparisons between normal distributions were performed using Student’s test, whereas distributions that could not be considered normal would be compared using the Wilcoxon rank sum test. Statistical significant differences were assumed for .
3. Results
For the simulated data and clinical data set, ECG segments of 128 consecutive beats were randomly extracted and directly submitted to the AMFM, which does not require preprocessing [11]. Rather, a data preprocessing stage, described in Section 2.1, was performed prior to submitting the CM and the HAM.
3.1. Simulated Cases
For the simulated cases, results obtained from TWA analysis, by applying the CM, the AMFM, and the HAM, respectively, are reported in the tables below.
In Table 1 the results obtained from the simulated ECG tracing with no TWA (N_TWA) are reported. These three methods applying to tracings with no baseline yielded an accurate identification of TWA amplitude. In the presence of 0.30 and 1.50 Hz baseline wandering, a slight overestimation of TWA amplitude was produced by the CM. In the presence of baseline fluctuations with a frequency equal (0.71 Hz) to that of TWA, the strong overestimations of TWA amplitude were produced by the three methods.

In Table 2 the results obtained from the simulated ECG tracing with stationary TWA (S_TWA) are reported. In the presence of 0.30 and 1.50 Hz baseline wandering, the CM and the AMFM produced underestimation of TWA amplitude for different stationary TWA, while the HAM yielded an accurate identification of TWA (). In the presence of baseline fluctuations at the TWA frequency of 0.71 Hz, the CM produced underestimation of TWA amplitude and the AMFM produced strong overestimation of TWA amplitude. While the HAM, produced a slight underestimation of TWA amplitude, showed a better ability to quantifying TWA amplitude in this case, and obtained for S_TWA10, for S_TWA50, for S_TWA100, respectively.

For TV_TWA1 and TV_TWA2 cases, the local TWA comparisons are considered because of timevarying amplitudes. A graphical representation of the results obtained from ECG simulations with the presence of timevarying TWA (TV_TWA1 and TV_TWA2) is depicted in Figure 8. The columns of panels from left to right display simulated TWAamplitude signals (128 beats) and detected TWAamplitude signals provided by the CM, the AMFM, and the HAM, respectively. For the cases of the simulated ECG tracing with 0.30 and 0.71 Hz baseline wandering, analogous results are obtained.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
(o)
(p)
The root mean square errors obtained are reported in Table 3. The three methods were able to track the time course of TWA. But the local TWAamplitude signals provided by the CM showed vigorous amplitude fluctuation, and are higher then and uniformly. The CM and the AMFM produced underestimation of TWA amplitude, which are the same as the above mentioned cases, while the HAM provided a good estimate of TWA (, except the case of frequency of baseline equal to that of TWA).

In Table 4 the results obtained from the simulated ECG tracing with phasereversal TWA (PR_TWA) are reported. The AMFM produced underestimation of TWA amplitude (40%) in the presence of 0.30 and 1.50 Hz baseline wandering, while the CM and the HAM produced good results (, ). In the presence of baseline fluctuations at the TWA frequency of 0.71 Hz, the three methods produced strong overestimation of TWA amplitude, but obviously the results provided by the HAM are more close to the simulated TWA (, ).

3.2. Clinical Cases
TWA levels quantified by the three competing methods in the Hsubjects and patients data are reported in Table 5. The CM, the AMFM, and the HAM detected various levels of TWA in the same Hsubjects and all patients. TWA was detected in two Hsubjects by the CM and the AMFM, while only one Hsubject was affected by TWA according to the HAM (Table 5). And the three methods detected the presence of TWA in all patients. TWA showed a normal distribution over patients’ populations. Mean TWA values estimated by the HAM in Hsubjects () and patients () were higher than the corresponding mean TWA estimates provided by the AMFM (Hsubjects: ; patients: ) and the CM (Hsubjects: ; patients: ). All these methods provided mean TWA estimates which showed significant differences between Hsubject and patient groups.
 
when comparing Hsubjects versus patients with the test for normal distributions. 
The CM, the AMFM, and the HAM detected the presence of TWA in all patients and provided similar TWA estimates. The CM and the AMFM tend to underestimate TWA (Figure 8 and simulation study results), and this finding is confirmed by our clinical result.
4. Discussion
In this study four simulated cases were generated with characters of absence of TWA; presence of different kinds of stationary TWA; presence of two kinds of nonstationary (timevarying) TWA; and presence of phasereversal TWA. The other two timedomain methods, namely, the CM and the AMFM, are compared with the HAM in TWA detection. Results of our simulation study indicate that the HAM allows detection and quantification of TWA better than the CM and the AMFM.
The CM was found to underestimate TWA amplitude in the simulated ECG tracing, since it assumed TWA being distributed along the entire length of the Twave [19]. And in the case of ECG simulations with the presence of timevarying TWA, the CM produced the worst results compared with other methods (Figure 8 and Table 3).
The AMFM showed good performance of timevarying TWA detection, due to that its heartrate adaptivematchfilter yielded the suppression of all ECG and interferences frequency components, while it produced strong overestimation of TWA amplitude in the presence of baseline fluctuations at the TWA frequency, and the reason and a potential solution were given in the literature [10].
We can find that the HAM yielded, in general, a more accurate TWA estimation in the simulated cases, although in the presence of baseline fluctuations with frequency equal to that of TWA the deviation from TWA amplitude was produced which are also produced by the CM, and the reason is that the accuracy of isoelectric line estimation by the cubic spline interpolation technique reduces. And all simulation cases showed that were systematically smaller than and , even in the presence of baseline fluctuations at the TWA frequency of 0.71 Hz.
The HAM performs an amplitude corrections procedure based on the linear least squares fitting technique before calculating the local TWA, which further suppresses the interferences, and the local threshold criterion, integrated in the HAM, appears to help improve detecting accuracy. The limitation of the CM is that when computing the ACI, the exact location of the maximum amplitude difference between the two waves is lost, so that a mean (over Twave) TWA amplitude value is provided (assumption of uniformly distributed TWA), while in our method TWA is measured by the maximum absolute value of the difference between the corrected matrixes for odd and even Twaves, which also improves the accuracy of TWA estimation. The baselines with various frequencies are considered in the simulated cases, and the test results also show that HAM is robust to the noise.
Our results relative to the clinical data highlighted consistency in the detection and quantification of TWA by the three different methods, and significant differences between Hsubject and patient groups are manifested, as shown in Table 5, while the TWA amplitudes measured by the CM and the AMFM are slightly lower than that by the HAM. The results of our simulation test help interpreting the TWA data obtained from clinical cases.
5. Conclusions
A novel timedomain TWA detector is presented in this paper based on the correlation method and linear least squares fitting technique. Although the method is simple, it was validated using simulated ECG test signals with artificial TWA of various amplitudes and baseline wanderings and achieved good performance under reasonable levels of noise. The results of our simulation study indicate that the HAM provides a more accurate TWA estimation than the CM and the AMFM.
Results of TWA detection produced by the three methods in real clinical ECG records show high consistency, which confirms the TWA detection power of the hybrid method for clinical data, although the quantifying TWA amplitudes by the HAM are universally higher than that by the CM and the AMFM.
Ethical Approval
The study was approved by the Institutional Research Ethics Committee of Liuhuaqiao Hospital, Guangzhou, Approval no. 20111212. Informed consent was obtained from each subject. The study protocol conforms to the ethical guidelines of the World Medical Association, Declaration of HelsinkiEthical Principles for Medical Research Involving Human Subjects adopted by the 18th WMA General Assembly, Helsinki, Finland, June 1964, as revised in Tokyo 2004, as reflected in a priori approval by the appropriate institutional review committee. Declaration of HelsinkiEthical Principles for Medical Research Involving Human Subjects adopted by the 18th WMA General Assembly, Helsinki, Finland, June 1964, as revised in Tokyo 2004, as reflected in a priori approval by the appropriate institutional review committee.
Conflict of Interests
All authors of the present work exclude any financial and personal relationships with other people or organizations that could inappropriately influence this job.
Acknowledgment
This work was supported by the National Nature Science Foundation of China (no. 60901027).
References
 D. R. Adam, S. Akselrod, and R. J. Cohen, “Estimation of ventricular vulnerability to fibrillation through Twave time series analysis,” Computing in Cardiology, vol. 8, pp. 307–310, 1981. View at: Google Scholar
 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. View at: Google Scholar
 B. D. Nearing, A. H. Huang, and R. L. Verrier, “Dynamic tracking of cardiac vulnerability by complex demodulation of the T wave,” Science, vol. 252, no. 5004, pp. 437–440, 1991. View at: Google Scholar
 P. Laguna, M. Ruiz, and G. B. Moody, “Repolarization alternans detection using the KL transform and the beatquency spectrum,” Computing in Cardiology, vol. 23, pp. 673–676, 1996. View at: Google Scholar
 B. D. Nearing and R. L. Verrier, “Modified moving average analysis of Twave alternans to predict ventricular fibrillation with high accuracy,” Journal of Applied Physiology, vol. 92, no. 2, pp. 541–549, 2002. View at: Google Scholar
 L. Burattini, W. Zareba, and A. J. Moss, “Correlation method for detection of transient Twave alternans in digital holter ECG recordings,” Annals of Noninvasive Electrocardiology, vol. 4, no. 4, pp. 416–424, 1999. View at: Google Scholar
 L. Burattini, S. Bini, and R. Burattini, “Comparative analysis of methods for automatic detection and quantification of microvolt Twave alternans,” Medical Engineering and Physics, vol. 31, no. 10, pp. 1290–1298, 2009. View at: Publisher Site  Google Scholar
 V. Monasterio, P. Laguna, and J. P. Martínez, “Multilead analysis of Twave alternans in the ecg using principal component analysis,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 7, pp. 1880–1890, 2009. View at: Publisher Site  Google Scholar
 J. P. Martínez and S. Olmos, “Methodological principles of T wave alternans analysis: a unified framework,” IEEE Transactions on Biomedical Engineering, vol. 52, pp. 599–613, 2005. View at: Google Scholar
 L. Burattini, W. Zareba, and R. Burattini, “Automatic detection of microvolt Twave alternans in Holter recordings: effect of baseline wandering,” Biomedical Signal Processing and Control, vol. 1, no. 2, pp. 162–168, 2006. View at: Publisher Site  Google Scholar
 L. Burattini, W. Zareba, and R. Burattini, “Adaptive match filter based method for time vs. amplitude characterization of microvolt ECG Twave alternans,” Annals of Biomedical Engineering, vol. 36, no. 9, pp. 1558–1564, 2008. View at: Publisher Site  Google Scholar
 C. R. Meyer and H. N. Keiser, “Electrocardiogram baseline noise estimation and removal using cubic splines and statespace computation techniques,” Computers and Biomedical Research, vol. 10, no. 5, pp. 459–470, 1977. View at: Publisher Site  Google Scholar
 J. P. Martínez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, “A waveletbased ECG delineator evaluation on standard databases,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, pp. 570–581, 2004. View at: Publisher Site  Google Scholar
 S. M. Narayan and J. M. Smith, “Spectral analysis of periodic fluctuations in electrocardiographic repolarization,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 2, pp. 203–212, 1999. View at: Publisher Site  Google Scholar
 R. L. Verrier, T. Klingenheben, M. Malik et al., “Microvolt Twave alternans: physiological basis, methods of measurement, and clinical utilityconsensus guideline by international society for Holter and noninvasive Electrocardiology,” Journal of the American College of Cardiology, vol. 58, no. 13, pp. 1309–1324, 2011. View at: Publisher Site  Google Scholar
 J. P. Martínez, S. Olmos, G. Wagner, and P. Laguna, “Characterization of repolarization alternans during ischemia: timecourse and spatial analysis,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 4, pp. 701–711, 2006. View at: Publisher Site  Google Scholar
 G. B. Moody, “The physionet / computers in cardiology challenge 2008: Twave alternans,” Computers in Cardiology, vol. 35, pp. 505–508, 2008. View at: Google Scholar
 L. Burattini, S. Bini, and R. Burattini, “Correlation method versus enhanced modified moving average method for automatic detection of Twave alternans,” Computer Methods and Programs in Biomedicine, vol. 98, no. 1, pp. 94–102, 2010. View at: Publisher Site  Google Scholar
 L. Burattini, S. Bini, and R. Burattini, “Automatic microvolt Twave alternans identification in relation to ECG interferences surviving preprocessing,” Medical Engineering and Physics, vol. 33, no. 1, pp. 17–30, 2011. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2014 Xiangkui Wan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.