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C H A P T E R 7

The Pathophysiology GuidedAssessment of T-Wave Alternans

Sanjiv M. Narayan

7.1 Introduction

Sudden cardiac arrest (SCA) causes more than 400,000 deaths per year in the UnitedStates alone, largely from ventricular arrhythmias [1]. T-wave alternans (TWA) isa promising ECG index that indicates risk for SCA from beat-to-beat alternationsin the shape, amplitude, or timing of T waves. Decades of research now link TWAclinically with inducible [2–4] and spontaneous [5–7] ventricular arrhythmias, andwith basic mechanisms leading to their initiation [8, 9].

This bench-to-bedside foundation makes TWA a very plausible predictor ofsusceptibility to SCA, and motivates the need to define optimal conditions for itsdetection that are tailored to its pathophysiology. TWA has become a prominentrisk stratification method over the past 5 to 10 years, with recent approval forreimbursement, and the suggestion by the U.S. Centers for Medicare and MedicaidServices (CMS) for the inclusion of TWA analysis in the proposed national registryfor SCA management [10].

7.2 Phenomenology of T-Wave Alternans

Detecting TWA from the surface ECG exemplifies a bench-to-bedside bioengineer-ing solution to tissue-level and clinical observations. T-wave alternans refers toalternation of the ECG ST segment [3, 11], T wave and U wave [12], and has alsobeen termed repolarization alternans [4, 13]. Visible TWA was first reported in theearly 1900s by Hering [14] and Sir Thomas Lewis [15] and was linked with ven-tricular arrhythmias. Building upon reports of increasingly subtle TWA on visualinspection [16], contemporary methods use signal processing to extract microvolt-level T-wave fluctuations that are invisible to the unaided eye [17].

7.3 Pathophysiology of T-Wave Alternans

TWA is felt to reflect a combination of spatial [18] and temporal [8] dispersion ofrepolarization (Figure 7.1), both of which may be mechanistically implicated in theinitiation of ventricular tachyarrhythmias [19, 20].

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August 24, 2006 11:47 Chan-Horizon Azuaje˙Book

198 The Pathophysiology Guided Assessment of T-Wave Alternans

Figure 7.1 Mechanisms underlying TWA. Left: spatial dispersion of repolarization. Compared toregion 2, region 1 has longer APD and depolarizes every other cycle (beats 1 and 3). Right: temporaldispersion of repolarization. APD alternates between cycles, either from alternans of cytosolic calcium(not shown), or steep APD restitution. APD restitution (inset) is the relationship of APD to diastolicinterval (DI), the interval separating the current action potential from the prior one. If restitution issteep (slope >1), DI shortening abruptly shortens APD, which abruptly lengthens the next DI andAPD, leading to APD alternans.

Spatial variations in action potential duration (APD) or shape [18], or conduc-tion velocity [21, 22], may prevent depolarization in myocytes still repolarizing fromtheir last cycle (Figure 7.1, left, Region 1) and cause 2:1 behavior (alternans) [8].Moreover, this mechanism may allow unidirectional block at sites of delayed repo-larization and facilitate reentrant arrhythmias.

Temporal dispersion of repolarization (alternans of APD; Figure 7.1, right) mayalso contribute to TWA [18]. APD alternans has been reported in human atria [23]and ventricles [24, 25] and in animal ventricles [8], and under certain conditions,it has been shown to lead to conduction block and arrhythmias [8, 9].

APD alternans is facilitated by steep restitution. APD restitution expresses therelationship between the APD of one beat and the diastolic interval (DI) separatingits upstroke from the preceding action potential [24] the bottom right of Figure 7.1,bottom right). If APD restitution is steep (maximum slope >1), slight shorteningof the DI from a premature beat can significantly shorten APD, which lengthensthe following DI and APD and so on, leading to alternans [26]. By analogy, steeprestitution in conduction velocity [21, 22] can also cause APD alternans. Undercertain conditions [27], both may lead to wavefront fractionation and ventricularfibrillation (VF) [20] or, in the presence of structural barriers, ventricular tachycar-dia (VT) [9]. At an ionic level, alternans of cytosolic calcium [28, 29] may underlieAPD alternans [30] and link electrical with mechanical alternans [29, 31].

TWA may be perturbed by abrupt changes in heart rate [24] or ectopic beats[8, 24]. Depending on the timing of the perturbation relative to the phase of al-ternation, alternans magnitude may be enhanced or attenuated and its phase (ABAversus BAB; see Figure 7.2, top) maintained or reversed [8, 31, 32]. Under critical

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7.4 Measurable Indices of ECG T-Wave Alternans 199

Figure 7.2 Measurable indices of TWA include: (a) TWA magnitude; (b) TWA phase, which showsreversal (ABBA) towards the right of the image; (c) distribution of TWA within the T wave, that is,towards the distal T wave in this example; (d) time-course of TWA that clearly varies over severalbeats; and (e) TWA spatial orientation.

conditions, ischemia [33] or extrasystoles [31, 33] may reverse the phase of al-ternans in only one region, causing alternans that is out of phase between tissueregions (discordant alternans), leading to unidirectional block and ventricular fibr-illation [8].

7.4 Measurable Indices of ECG T-Wave Alternans

Several measurable indices of TWA have demonstrated clinical relevance, as shownin Figure 7.2. First, TWA magnitude is reported by most techniques and is the piv-otal index [Figure 7.2(a)]. In animal studies, higher TWA magnitudes reflect greaterrepolarization dispersion [9, 34] and increasing likelihood for ventricular arrhyth-mias [11, 35]. Clinically, TWA magnitude above a threshold (typically 1.9 mCVmeasured spectrally) is generally felt to reflect increased arrhythmia susceptibility[3, 6]. However, this author [36] and others [37] have recently shown that thesusceptibility to SCA may rise with increasing TWA magnitude.

The second TWA index is its phase [Figure 7.2(b)]. This may be detected bymethods to quantify sign-change between successive pairs of beats, or spectrally asa sudden fall in TWA magnitude that often coincides with an ectopic beat or other

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perturbation [38]. As discussed in Section 7.3, regional phase reversal of tissue al-ternans (discordant alternans) heralds imminent ventricular arrhythmias in animalmodels [8, 9]. Clinically, we recently reported that critically timed ectopic beats mayreverse ECG TWA phase, and that this reflects heightened arrhythmic susceptibil-ity. In 59 patients with left ventricular systolic dysfunction from prior myocardialinfarction, TWA phase reversal was most likely in patients with sustained arrhyth-mias at electrophysiologic study (EPS) and with increasingly premature ectopicbeats [13]. On a long-term 3-year follow-up, logistic regression analysis showedthat TWA phase reversal better predicted SCA than elevated TWA magnitude, sus-tained arrhythmias at EPS, or left ventricular ejection fraction [36]. TWA phasereversal may therefore help in SCA risk stratification, particularly if measured attimes of elevated arrhythmic risk such as during exercise, psychological stress, orearly in the morning.

Third, the temporal evolution of TWA has been reported using time- and fre-quency-domain methods [Figure 7.2(c)]. At present, it is unclear whether specificpatterns of TWA evolution, such as a sudden rise or fall, oscillations, or constantmagnitude, add clinical utility to the de facto approach of dichotomizing TWA mag-nitude at some threshold. Certainly, transient peak TWA following abrupt changesin heart rate [4] is less predictive of clinical events than steady-state values attainedafter the perturbation. Because of restitution, APD alternans is a normal responseto abrupt rate changes in control individuals as well as patients at risk for SCA [24].However, at-risk patients may exhibit a steeper slope and different shape of restitu-tion (Figure 7.1, bottom right) [39], leading to prolonged TWA decay after transientrises in heart rate compared to controls. Moreover, we recently reported prolongedTWA decay leading to hysteresis, such that TWA magnitude remains elevated afterheart rate deceleration from a faster rate, in at-risk patients but not controls [4].This has been supported by animal studies [40]. Finally, TWA magnitude may oscil-late at any given rate, yet the magnitude of oscillations may be inversely related toTWA magnitude [41]. Theoretically, therefore, the analysis of TWA could be con-siderably refined by exploiting specific temporal patterns of TWA at steady-stateand during perturbations.

Fourth, the distribution of TWA within the T wave also indicates arrhythmic risk[Figure 7.2(d)], and is most naturally detected with time-domain techniques [42].Theoretically, the terminal portions of the T wave reflect the trailing edge of repo-larization, which, if spatially heterogeneous, may enable unidirectional conductionblock and facilitate reentrant ventricular arrhythmias. Indeed, pro-arrhythmic inter-ventions in animals cause APD alternans predominantly in phase III, correspondingwith the T-wave terminus. In preliminary clinical studies, we reported that pro-arrhythmic heart rate acceleration [42] and premature ectopic beats caused TWAto distribute later within the T wave [13], particularly in individuals with induciblearrhythmias at EPS [13]. One potentially promising line of investigation would be todevelop methods to quantify whether TWA distribution within the T wave indicatesspecific pathophysiology and different outcomes. For example, data suggests thatacute ischemia in dogs causes “early” TWA (in the ST segment) [11], which mayportend a different prognosis than “late” TWA (distal T wave) in patients withsubstrates for VT or VF but without active ischemia [42].

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7.5 Measurement Techniques 201

Fifth, the spatial distribution of TWA has recently been studied by analyzingTWA vector between ECG leads [Figure 7.2(e)]. Pathophysiologically, alternans oftissue APD is distributed close to scar in animal hearts [9], and ECG TWA dur-ing clinical coronary angioplasty overlies regional ischemic zones [43]. We recentlyreported that ECG TWA magnitude, in patients with prior MI but without ac-tive ischemia, is greatest in leads overlying regions of structural disease definedby echocardiographic wall motion abnormalities [44]. In addition, Verrier et al.reported that TWA in lateral ECG leads best predicted spontaneous clinical ar-rhythmias in patients with predominantly lateral prior MI [45], while Klingenhebenet al. reported that patients with nonischemic cardiomyopathy at the greatest riskfor events were those in whom TWA was present in the largest number of ECGleads [37]. Methods to more precisely define the regionality of TWA may improvethe specificity of TWA for predicting SCA risk.

7.5 Measurement Techniques

Several techniques have been applied to measure TWA from the surface ECG. Eachtechnique poses theoretical advantages and disadvantages, and the optimal methodfor extracting TWA may depend upon the clinical scenario. TWA may be mea-sured during controlled sustained heart rate accelerations, during exercise testing,controlled heart rate acceleration during pacing, uncontrolled or transient exercise-related heart rate acceleration in ambulatory recordings, and from discontinuitiesin rhythm such as ectopic beats. At the present time, few studies have comparedmethods for their precision to detect TWA between these conditions, or the predic-tive value of their TWA estimates for meaningful clinical endpoints.

Martinez and Olmos recently developed a comprehensive “unified framework”for computing TWA from the surface ECG [17], in which they classified TWA de-tection into preprocessing, data reduction, and analysis stages. This section focusesupon the strengths and limitations of TWA analysis methods, broadly compris-ing short-term Fourier transform (STFT)–based methods (highpass linear filtering),sign-change counting, and nonlinear filtering methods.

7.5.1 Requirements for the Digitized ECG Signal

The amplitude resolution of digitized ECG signals must be sufficient to measureTWA as small as 2 mcV (the spectrally defined threshold [38]). Assuming a dynamicrange of 5 mV, 12-bit and 16-bit analog-to-digital converters provide theoreticalresolutions of 1.2 mcV and < 0.1 mcV, respectively, lower than competing noisesources. The ECG sampling frequency of most applications, ranging from 250 to1,000 Hz, is also sufficient for TWA analysis. Some time-domain analyses for TWAfound essentially identical results for sampling frequencies of 250 to 1,000 Hz,with only slight deterioration with 100-Hz sampling [46]. For ambulatory ECGdetection, frequency-modulated (FM) and digital recorders show minimal distortionfor heart rates between 60 to 200 bpm, and a bandpass response between 0.05 and50 Hz has been recommended [47].

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7.5.2 Short-Term Fourier Transform–Based Methods

These methods compute TWA from the normalized row-wise STFT of the beat-to-beat series of coefficients at the alternans frequency (0.5 cycle per beat). Examplesinclude the spectral [2, 32, 48] and complex demodulation [11] methods. The spec-tral method is the basis for the widely applied commercial CH2000 and HeartWavesystems (Cambridge Heart Inc., Bedford, Massachusetts) and, correspondingly, hasbeen best validated (under narrowly defined conditions).

In general, STFT methods compute the detection statistics using a preprocessedand data reduced matrix of coefficients Y = {yi ( p)} as Yw [p, l]:

Yw [p, l] =∞∑

i=−∞yi [p]w [i − l] (−1)i (7.1)

where Yw [p, l] is the alternans statistic of length l, Y = {yi ( p)} is the reducedcoefficient matrix derived from the ECG beat series, and w(i) is the L-beat analysiswindow of beat-to-beat periodicity (periodogram). This is equivalent to highpasslinear filtering [17].

For the spectral method, the TWA statistic z can be determined by applyingSTFT to voltage time series [2, 32] or derived indices such as coefficients of theKarhunen-Loeve (KL) transform (see Chapter 9 in this book) [49]. The statistic isthe 0.5 cycle per beat bin of the periodogram, proportion to the squared modulusof the STFT:

zl[p] = 1L

|Yw [p, l]|2 (7.2)

For complex demodulation (CD), the TWA statistic z can also be determinedfrom voltage time series [11] or coefficients of the KL transform (KLCD) [17] asthe magnitude of the lowpass filtered demodulated 0.5 cycle/beat component:

zl[p] = ∣∣yl [p] ∗ hhpf [l]∣∣ (7.3)

where hhpf [k] = hlpf [k] · (−1)k is a highpass filter resulting from frequency transla-tion of the lowpass filter. Complex demodulation results in a new detection statisticfor each beat.

The spectral method is illustrated in Figure 7.3. In Figure 7.3(a), ECGs arepreprocessed prior to TWA analysis. Beats are first aligned because TWA may belocalized to parts of the T wave, and therefore lost if temporal jitter occurs betweenbeats. We and others have shown that beat alignment for TWA analysis is bestaccomplished by QRS cross-correlation [17, 32]. Beat series are then filtered andbaseline corrected to provide an isoelectric baseline (typically the T-P segment) [17].Successive beats are then segmented to identify the analysis window, typicallyencompassing the entire JT interval (shown) [32]. Unfortunately, the literature israther vague on how the T-wave terminus is defined, largely because several meth-ods exist for this purpose yet none has emerged as the gold standard [50]. Afterpreprocessing, alternans at each time point [arrow in Figure 7.3(a)] is manifest asoscillations over successive T waves. Fourier analysis then results in a large ampli-tude spectral peak at 0.5 cycle/beat (labeled �T). Time-dependent analysis separates

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Figure 7.3 (a) Spectral computation of TWA. In aligned ECG beats, alternans at each time pointwithin the T wave (vertical arrows) results in down-up-down oscillations. Fourier transform yieldsa spectrum in which alternans is the peak at 0.5 cycle/beat peak (�T). In the final spectrum(summated for all time points), �T is related to spectral noise to compute Val t and k-score [seepart (b)]. (b) Positive TWA (from HeartWave system, Cambridge Heart, Inc.) shows (i) Val t ≤ 1.9mcV in two precordial or one vector lead (here Val t ≈ 46 mcV in V3-V6) with (ii) k-score ≥ 3(gray shading) for > 1 minute (here ≈ 5 minutes), at (iii) onset rate < 110 bpm (here 103 bpm),with (iv) < 10% bad beats and < 2 mcV noise, without (v) artifactual alternans. Black horizontal barsindicate periods when conditions for positive TWA are met.

time points within the T wave (illustrated), and allows TWA to be temporally lo-calized within the T wave. However, to provide a summary statistic, spectra aresummated across the T wave (detection window L). Finally, TWA is quantified byits (1) voltage of alternation (Valt) equal to (�T-spectral noise)/T wave duration;and (2) k-score (TWA ratio), equal to �T/noise standard deviation.

7.5.3 Interpretation of Spectral TWA Test Results

Since TWA is rate related, it is measured at accelerated rates during exercise orpacing, while maintaining heart rate below the threshold at which false-positiveTWA may occur in normal individuals from restitution (traditionally, 111 bpm)[42, 51]. Criteria for interpreting TWA from the most widely used commercialsystem (Cambridge Heart, Bedford, Massachusetts) are well described [38]. PositiveTWA, illustrated in Figure 7.3(b), is defined as TWA sustained for > 1 minute withamplitude (Valt) ≥ 1.9 mcV in any vector ECG lead (X, Y, Z) or two adjacentprecordial leads, with k-score > 3.0 and onset heart rate < 110 bpm, meeting noise

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Figure 7.3 (continued.)

criteria of < 10 % ectopic beats, < 2 mcV spectral noise, and absence of artifactualalternans from respiratory rate or RR interval alternans.

Notably, the optimal TWA magnitude cutpoint for predicting sudden deathrisk has been questioned. We authors [4] and others [52] have used custom andcommercial spectral methods, respectively, to show that higher cutpoints of 2.6and 3 mcV better predict clinical endpoints. A recent study confirmed that TWAmagnitude ≥2.9 mcV was more specific for predicting sudden death [53].

7.5.4 Controversies of the STFT Approach

The major strength of STFT is its sensitivity for stationary signals. Indeed, in simu-lations [32] and subsequent clinical reports during pacing [3, 13, 36, 54], spectralmethods can detect TWA of amplitudes ≤ 1 mcV [3, 13, 36, 54]. It has yet tobe demonstrated whether alternative techniques including time-domain nonlinearfiltering (described below) achieve this sensitivity on stationary signals.

However, STFT also has several drawbacks. Primarily, the linear filtering in-volved in STFT methods is sensitive to nonstationarity of the TWA signal within

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the detection window. The detection window (L) ranges in duration from 30 [55]to 128 [3] beats, which represents 16 to 77 seconds at rates of 100 to 110 bpm.Nonstationarity over this time course may reflect changing physiology at constantheart rate, rate-related fluctuations, or noise. Although it has been suggested thatalternative STFT methods such as complex demodulation may better track transientTWA [55], all linear filtering methods have theoretical limitations for nonstationarysignals, and differences in their ability to track TWA “transients” can be minimizedas demonstrated by Martinez and Olmos [17].

By extension, STFT methods are also adversely influenced by rhythm disconti-nuities, including abrupt changes in heart rate, or atrial or ventricular ectopy. Notonly can ectopy reverse the true phase of TWA, as described in Sections 7.3 and7.4, but an ectopic beat may technically degrade the STFT computation of TWA,depending upon its phase relationship, by introducing an impulse to the powerspectrum as we have shown [32]. Beat deletion and substitution are typically usedto eliminate ectopy [56], yet the best strategy requires knowledge of the phase TWArelative to the position of the ectopic beat. Deletion is preferred if the ectopic re-verses TWA phase, while substitution is preferred if phase is maintained [32]. Wehave demonstrated both types of behavior following premature beats in patientsat risk for ventricular arrhythmias [13] (see Figure 7.4), in whom phase reversalindicated a worse outcome [36].

7.5.5 Sign-Change Counting Methods

These methods use a strategy that counts sign-changes or zero-crossings from beat tobeat. The Rayleigh test [57] measures the regularity of the phase reversal pattern to

Figure 7.4 Extrasystoles (S2) and TWA phase. The top shows stylized voltage alternation at onetime point within the STU segment. Extrasystoles (S2) may leave phase (a) unaltered or (b) reversedin the subsequent oscillation. Inset panels depict each case using actual mid-STU data for three beatspreceding and following S2.

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determine if a beat series is better explained by a random distribution or a periodicpattern, with sign reversal indicating alternate-beat periodicity.

The ECG or derived parameter series is analyzed using a sliding window ofbeats. In each data block, the number of deviations relative to alternation (i.e.,yi > yi+1; yi+1 < yi+2 . . . , or the opposite phase) is measured, and a significanceis assigned that reflects the probability of obtaining such a pattern from a randomvariable. A given significance value is associated with a fixed threshold λZ in thenumber of beats following one of the patterns. Therefore, TWA is deemed pres-ent if

Zl = 12

(L + |

l∑i=l−L+1

sign (�yi ) (−1)i |)

≥ λZ (7.4)

where {�yi } = {yi − yi−1}. STFT can now be applied to the sign of the series,although the nonlinearity of sign analysis limits the effect of outliers in the detectionstatistic, unlike true STFT-based methods. Notably, however, amplitude informationis lost in sign analysis [17].

The correlation method modifies sign-counting in that the alternans correlationindex yi [in (7.4)] is usually near one, since ST-T complexes are similar to thetemplate. When TWA is present, the correlation alternates between values >1 and< 1. Burattini et al. [58] used consecutive sign changes in the series to decide thepresence of TWA.

The Rayleigh test and the correlation method are highly dependent upon thelength of the analysis window. In their favor, short counting windows (as in the cor-relation method) facilitate the detection of brief TWA episodes, enabling TWA tobe detected from short ECG recordings, or its time course to be defined sequentiallywithin ambulatory ECGs. However, short windows increase the likelihood that ran-dom sequences will falsely be assigned as alternans [17]. Moreover, the reliabilityof both methods requires the signal to have a dominant frequency (the alternanscomponent) and a high signal-to-noise ratio. Unfortunately, high amplitude com-ponents such as respiration, baseline wander, or slow physiological variations canseriously degrade their performance. These observations may limit the applicabilityof these methods [17].

7.5.6 Nonlinear Filtering Methods

Nonlinear filtering methods have recently been described that likely improve theability to detect TWA in the presence of nonstationarities and ectopic beats. Thesemethods include the modified moving average method (MMA) [59], which wasrecently incorporated commercially into the CASE-8000 electrophysiology system(GE Marquette, Inc., Milwaukee, Wisconsin), and the Laplacian Likelihood Ratio(LLR) [60].

Verrier et al. [45] have described the MMA method that creates parallel averagesfor designated even (A) and odd (B) “beats” (JT segments), defined as

ECG beat An (i) = ECG beat2n(i) (7.5)

ECG beat Bn (i) = ECG beat2n−1(i) (7.6)

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7.6 Tailoring Analysis of TWA to Its Pathophysiology 207

where i = 1, . . . is the number of samples per beat, n = 1, 2, . . . N/2, and N is thetotal number of beats in the data segment.

Modified moving average complexes A and B are initialized with the first evenand odd ECG beats in the sequence, respectively. The next modified moving averagecomputed beat is formed using the present MMA beat and the next ECG beat. Ifthe next ECG beat has larger amplitude than the present MMA computed beat, thenext MMA computed beat value is made higher than the present MMA computedbeat value; the reverse occurs if the next ECG beat is smaller than the present MMAcomputed beat. Increment and decrements are nonlinear, to minimize the effects ofoutlying beats. As described by the authors [45]:

Computed beat An(i) = Computed beat An−1(i) + �A (7.7)

where

�A = −32 if η ≤ −32

�A = −η if −1 ≥ η > −32

�A = −1 if 0 > η > −1

�A = 0 if η = 0

�A = 1 if 1 ≥ η > 0

�A = η if 32 ≥ η > 1

�A = 32 if η > 32

where η = [ECG beat An−1(i) – Computed beat An−1 (i) / 8] and n is the beatnumber within series A. The parallel computation is performed for beats of type B.TWA is then computed as

TWA = max i=Twaveendi=J point |BeatBn (i) − BeatAn (i)| (7.8)

When beat differences are small, the method behaves linearly. However, nonlin-earity limits the effect of abrupt changes, artifacts, and anomalous beats. In a recentmodeling study, MMA effectively determined TWA in signals with premature beats,while spectral methods attenuated TWA at points of discontinuity reflecting detec-tion artifact and TWA phase reversal (Figure 8 in [17]).

Other nonlinear methods are based upon the median beat, including the LLR[60], in which the individual statistic is proportional to the absolute sum of valuesof the demodulated series lying between 0 and the maximum likelihood estimatorof the alternating amplitude (described in detail in [17]). This computation takesthe form of an STFT with a rectangular window, where some extreme elements arediscarded. Again, the nonlinearity inherent in this approach makes it robust in theface of outliers and noise from discontinuities and ectopic beats.

7.6 Tailoring Analysis of TWA to Its Pathophysiology

Despite the many approaches described to compute TWA [17], few studies havecompared methods for the same clinical dataset, or validated them against clinical

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endpoints. Moreover, TWA varies with physiologic conditions, yet it is presentlyunclear which measurement approach—or physiologic milieu—optimally enablesTWA to stratify SCA risk. This is true whether measuring TWA magnitude, TWAphase, the distribution of TWA within the T wave, or the temporal evolution andspatial distribution of TWA.

7.6.1 Current Approaches for Eliciting TWA

Early studies showed that TWA magnitude rises with heart rate in all individuals, butat a lower threshold in patients at presumed risk for SCA than controls [4, 42, 54].As a result, TWA is typically measured during acceleration while maintaining heartrates < 111 bpm [38] to minimize false-positive TWA from normal rate-respon-siveness. It remains unclear whether the onset heart rate criterion <111 bpm is op-timal [38], since studies suggest that TWA at lower onset heart rates (90 to 100 bpm)better predicts SCA [53, 61]. Studies that define receiver operating characteristicsof onset heart rate of TWA for predicting SCA would be helpful.

An exciting recent development has been to detect TWA from ambulatory ECGrecordings [45] at times of maximum spontaneous heart rate (likely reflecting exer-cise or psychological stress), times of maximum ST segment shift (possibly reflectingclinical or subclinical coronary ischemia), and early morning (8 a.m.), when the SCArisk is elevated [45]. The investigators showed that TWA identified patients at riskfor SCA when analyzed at maximum spontaneous heart rate and at 8 a.m., butnot during maximum ST segment shift. Intuitively, ambulatory recordings providea satisfying, continuous, and convenient approach for analyzing TWA, and shouldperhaps become the predominant scenario for detecting nonstationary TWA.

7.6.2 Steady-State Rhythms and Stationary TWA

This is the simplest clinical scenario that may apply during cardiac pacing, and itlends itself readily to spectral analysis. The seminal clinical reports of Smith et al.[2] and Rosenbaum et al. [3] determined TWA in this fashion, while subsequentreports confirmed that elevated TWA magnitude correlates with induced [2, 3, 13,54] and spontaneous [3, 36] ventricular arrhythmias, particularly if measured atheart rates of 100 to 120 bpm [4].

Moreover, we demonstrated that TWA magnitude exhibits rate-hysteresis [4],and is therefore higher after deceleration to a particular rate than on accelerationto it. This has been supported by mechanistic studies [40] and suggests that TWAmagnitude should be measured during constant heart rate.

Rosenbaum et al. compared spectral with complex demodulation methods forsteady-state TWA and showed that TWA better predicted the results of EPS whenmeasured spectrally [62]. In recent preliminary studies, we compared TWA usingspectral and MMA methods in 224 ECG lead recordings during constant pacing ata rate of 110 bpm in 43 patients with mean LVEF 32 ± 9% and coronary disease.In ECGs where TWA was measurable by both methods (n = 102), MMA amplifiedTWA magnitude (Valt) by approximately three-fold compared to the spectral methodin all axes (for example, 13.4 ± 10.0 versus 4.3 ± 7.5 mcV in the x-axis, p =0.004; see Figure 7.5). This supports recent reports by Verrier et al. [45] that TWA

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7.6 Tailoring Analysis of TWA to Its Pathophysiology 209

Figure 7.5 Relationship of MMA and spectral TWA in the same ECGs. Both metrics were correlatedbut MMA increased TWA amplitude compared to spectral TWA (n = 102 ECGs). Not shown aren = 122 ECGs where MMA yielded alternans yet TWA was spectrally undetectable.

magnitude from MMA (Valt ≈ 45 mcV) is larger than from spectral methods (typicalValt 2 to 6 mcV [13, 42, 54]).

However, MMA in our studies also yielded TWA in an additional 122 ECGleads in which TWA was undetectable using the spectral method. We are performinglong-term follow-up on these patients to determine whether signal amplification byMMA reduces the specificity of TWA for clinical events compared to spectral TWA.

Importantly, it is now recognized that TWA amplitude oscillates even duringconstant rate pacing, by up to 10 mcV in a quasi-periodic fashion with a periodof approximately 2 to 3 minutes [41]. Thus, TWA is likely nonstationary underall measurement conditions. This has significant implications for the selection anddevelopment of optimal measurement techniques.

7.6.3 Fluctuating Heart Rates and Nonstationary TWA

Analysis of time-varying TWA poses several problems. First, STFT methods areless robust than nonlinear filtering (and sign change) approaches for nonstationaryTWA.

Second, it is unclear at which time period TWA should be analyzed. Certainly,TWA should be measured below heart rates likely to cause false-positive TWA innormal controls (<111 bpm [38]). However, it is unclear what rates of accelerationor deceleration are acceptable. We have shown that TWA magnitude rises faster, anddecays slower, in patients at risk for SCA than controls [4], and these dynamics mayhave prognostic significance. Indeed, measuring TWA during deceleration may leadto elevated TWA estimates due to hysteresis [4, 40], yet current practice measuresTWA at any time without abrupt heart rate change, and largely disregard differencesbetween acceleration and deceleration [38].

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A more central question is: Under what (patho)physiologic conditions is TWAmost predictive for SCA? TWA has traditionally been measured during controlledheart rate changes, at exercise test, or during pacing [16], yet spontaneous fluctu-ations may better reflect autonomic and hemodynamic conditions leading to theinitiation of spontaneous arrhythmias and SCA. TWA was recently computed usingMMA in fluctuating conditions during ambulatory ECGs, and successfully pre-dicted SCA risk [45].

Therefore, the robustness of MMA and other nonlinear filtering approaches tononstationarity and ectopic beats may make them more suitable than spectral anal-ysis for detecting nonstationarities in TWA, such as during heart rate accelerationversus deceleration, during fluctuations in heart rate, and from ambulatory ECGs.Studies are required to validate these methods comparatively against intracardiacindices of repolarization dispersion, and to compare their utility for clinical riskprediction.

7.6.4 Rhythm Discontinuities, Nonstationary TWA, and TWA Phase

Despite the extensive literature describing action potential duration restitution andalternans [24, 39] following ectopic beats and abrupt heart rate changes, it remainsunclear how TWA and its clinical utility are affected by discontinuities in rhythm.

Ectopic beats (during native rhythm) or extrastimuli (during pacing) alter thepathophysiology of TWA (Sections 7.3 and 7.4) and therefore influence the choiceof detection method. Detection of TWA phase is best achieved by sign-change meth-ods, although signal degradation of the spectral method (and other linear filters)from ectopy allows them to infer TWA phase reversal. For example, phase reversalis inferred if spectral TWA extinguishes at the time of an ectopic beat then reappearsrapidly, although traditional interpretation paradigms ignore this effect [38]. Para-doxically, the very fact that nonlinear filtering methods including MMA are largelyunaffected by ectopy [17] makes them less attractive for detecting phase reversal.

Abrupt changes in heart rate affect action potential duration [24, 39] and, ina sense, the first beat at a new rate represents a premature or postmature beat andwould be expected to influence TWA because of restitution. Increased TWA mag-nitude from rate acceleration, and hysteretic decay in magnitude with deceleration,are described above and are best detected using nonlinear rather than linear methods[17]. However, the time-evolution of TWA can be detected spectrally using over-lapping windows [4, 41], retaining the frequency resolution of the original segmentlength to which STFT is applied [17]. However, overlap introduces correlation be-tween consecutive TWA estimates that must be factored into interpretation schemes.

One special case of abrupt heart rate changes is the recent description of reso-nant pacing, where shortened cycle lengths are applied repeatedly to augment TWAmagnitude [63]. In preliminary studies, 11 patients with congestive heart failurewere paced using repeating 4-beat patterns of cycle lengths 535, 555, 555, 555 ms(resonant pacing, mean 550 ms) versus conventional pacing at cycle length 550ms. TWA magnitude (Valt) correlated closely for resonant and conventional pacing,yet resonant pacing significantly increased TWA magnitude and SNR. In this smallseries, resonant pacing also eliminated TWA phase reversals induced by prematurebeats. The investigators speculated that enhanced TWA magnitude may result from

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7.7 Conclusions 211

perturbed calcium cycling by the premature cycle. Further studies are required todetermine whether resonant pacing may better augment TWA using an interposedlong cycle (e.g., 565, 545, 545, 545 ms), to exploit TWA hysteresis [4, 40], whetherresonant pacing reduces TWA phase reversal in a larger series, whether such TWAshould be measured nonlinearly rather than spectrally, and how resulting TWAaugmentation affects its clinical utility for predicting SCA.

7.7 Conclusions

Of the numerous methods described to measure TWA, few have been applied toclinical datasets or validated against clinically meaningful endpoints. During steady-state, many methods may be similarly effective for computing TWA. However, TWAanalysis should increasingly focus upon its nonstationarities. Not only does TWAfluctuate even at constant heart rate, but TWA may better predict SCA if transientsare measured during extreme physiologic milieu or at times of ectopic beats. Non-linear methods to compute TWA are likely more robust to nonstationarity thanlinear (spectral) methods, and should therefore be developed and validated further.

Notably, the optimal detection of TWA may require combinations of approachesfor differing scenarios. Thus, linear approaches may be appropriate at relativelystable heart rate, nonlinear filtering may be preferred during rate fluctuations inambulatory ECGs, and sign-change methods may be required to examine TWAphase. Future work should also investigate the value of examining TWA in preferredspatial orientations, and how this may change with varying conditions. By tailoringTWA measurement to a more detailed understanding of its pathophysiology, therole of TWA is likely to broaden towards providing a more continuous assessmentof arrhythmic susceptibility.

Acknowledgments

This chapter was supported, in part, by a grant to Dr. Narayan from the NationalHeart, Lung, and Blood Institute (HL 70529).

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