IDENTIFICATION OF TIME-VARYING T-WAVE ALTERNANS

FROM 20-MINUTE ECG RECORDINGS

Issues Related to TWA Magnitude Threshold and Length of ECG Time Series

Laura Burattini

Department of Electromagnetics and Bioengineering, Polytechnic University of Marche, via Brecce Bianche

60131 Ancona, Italy

Wojciech Zareba

Heart Research Follow-Up Program, Cardiology Unit, Department of Medicine and Department of Biomedical

Engineering, University of Rochester, 601 Elmwood Ave, Rochester, NY 14642-8679, USA

Roberto Burattini

Department of Electromagnetics and Bioengineering, Polytechnic University of Marche, via Brecce Bianche

60131 Ancona, Italy

Keywords: Signal processing of the digital electrocardiographic signal, Repolarization variability, T-wave alternans.

Abstract: Aim of this study was the assessment of a T-wave alternans (TWA) identification procedure based on

application of an adaptive match filter (AMF) method, recently developed by ourselves, to a 20-minute

digital ECG recording (ECG20). Three-lead ECG20 tracings from 35 patients who survived an acute

myocardial infarction (AMI-group) and 35 healthy subjects (H-group) were analysed. The AMI-group

showed, on average, increased levels of TWA (P<0.01). Considering that noise may cause false positive

TWA detection, a threshold (THR

TWA

) was defined for TWA magnitude (TWAM) as the mean TWAM

+2SD over the H-group. TWAM exceeding this threshold identified a TWA-positive (TWA+) subject as

one at increased risk of sudden cardiac death. Fifteen (43%) AMI-patients vs. zero H-subjects were detected

as TWA+. This result meets clinical expectation. TWA manifested as a non stationary phenomenon that

could even be missed in all TWA+ subjects if our AMF (as well as any other technique) was applied to a

single short-term 128-beat ECG series, as usually done in previous reports. In conclusion, our AMF-based

TWA identification technique, applied to 20-minute ECG recordings, yields a good compromise between

reliability of time-varying TWA identification and computational efforts.

1 INTRODUCTION

T-wave alternans (TWA) is an electrophysiological

phenomenon which consists of two-to-one beat-to-

beat changes in the morphology (amplitude, shape

and, sometimes, polarity) of the electrocardiografic

(ECG) T wave. According to the literature, visible

and non-visible (microvolt) forms of TWA in ECG

recordings play an important role in the

arrhythmogenesis of failing myocardium (Schwartz

and Malliani, 1975; Zareba et al., 1994; Adam et al.,

1984; Smith et al., 1988; Rosembaum et al., 1994;

Kusmirek and Gold, 2007; Klingenheben and

Ptaszynski, 2007; Narayan, 2007). Visible forms of

TWA are infrequent. Non-visible TWA requires

computerized analysis of digital ECG recordings to

be recognized and parameterized in terms of

amplitude and duration. Thus, in the effort to assess

a clinically useful marker of sudden cardiac death,

development of methods for non-invasive automatic

detection of microvolt TWA has been a major

challenge in the last two decades (Rosenbaum et al.,

1996; Klingenheben et al., 2000; Ikeda et al., 2002;

Tapanainen et al., 2001;

Bigger and Bloomfield,

2007; Ikeda et al., 2006). Factors that may prevent a

reliable TWA quantification must be controlled by

186

Burattini L., Zareba W. and Burattini R. (2008).

IDENTIFICATION OF TIME-VARYING T-WAVE ALTERNANS FROM 20-MINUTE ECG RECORDINGS - Issues Related to TWA Magnitude Threshold

and Length of ECG Time Series.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 186-192

DOI: 10.5220/0001060601860192

 SciTePress

signal preprocessing, such as high frequency noise

filtering, detection of R peaks, RR stability testing,

and removal of baseline deviation from the

isoelectric line.

Recently, we developed a new adapting match

filter (AMF; Burattini et al., 2006) method for

automatic TWA detection, which, differently from

other reported techniques, does not require any pre-

processing of the ECG tracing, with the only

exception of R-peak detection. Making use of

simulated (Burattini et al., 2006) and experimental

(Burattini et al., 2007) data, we showed that this

method yields an improvement in reliability of TWA

detection over a previously reported correlation

method (Burattini,1998; Burattini et al., 1999).

Like any other TWA detection technique, our

AMF needs to be applied to ECG tracings with no

significant heart-rate variability and with a low noise

level. As a consequence, these techniques have

traditionally been applied to short-term ECG series,

typically consisting of 128 consecutive heart beats.

This rises the issue as to whether 128 beats portray

sufficient information on the presence of TWA. To

address this issue, in the present study we analyzed

3-lead (X,Y,Z) 20-minute digital ECG recordings

(ECG20). Our goal was to demonstrate that repeated

applications of our AMF-based method to several

tracings of 128 heart beats, within an ECG20, yields

a good compromise between reliability of TWA

identification and computational efforts. Our

analysis was performed on Holter ECG recordings

from patients who survived a myocardial infarction

since these are known to show increased levels of

TWA, compared to healthy subjects (Ikeda et al.,

2002; Pelicano et al., 2006; Ikeda et al., 2000; Puletti

et al., 1980).

2 METHODS

2.1 Clinical Data

Our study involved 35 healthy subjects (H-group;

RR=0.93±0.17 s) and 35 patients who survived an

acute myocardial infarction (AMI-group;

RR=0.88±0.14 s). For a better traceability during the

analysis, healthy subjects were identified as H01,

H02, … H35. Analogously, AMI patients were

identified as AMI01, AMI02, … AMI35.

A twenty-minute, three-lead (X,Y,Z) digital

Holter recording was obtained from each individual

in resting conditions, making use of Burdick

recorders (Burdick Inc., Milton, WI). Sampling rate

was 200 samples per sec. Series of 128 consecutive

cardiac beats were extracted every 10 seconds from

each tracing. Each series underwent our TWA

identification procedure as described below.

Because extraction of 128 cardiac beats every 10 s

causes a data overlap (on average 109 s for the H-

group, and 103 s for the AMI-group) between two

consecutive series, effects of this overlapping were

tested vs. an extraction procedure (data selection

every 128 beats) that avoids data overlapping.

2.2 T-Wave Alternans Detection by

Adaptive Match Filter

Our adaptive match filter method (AMF),

specifically designed to detect TWA (Burattini et al.,

2006 and 2007), was applied to each ECG series of

128 heart beats.

To avoid cases where TWA could be driven by

heart-rate variability (Adam et al., 1984;

Rosembaum et al., 1994; Burattini, 1998; Burattini

et al., 1999), an ECG time series has to be

characterized by a stable heart rate to be eligible for

TWA analysis. Specifically, we required that:

SDRR <0.1·MRR (1)

where MRR and SDRR are mean and standard

deviation of RR intervals (in s).

Under this condition, the TWA phenomenon is

assumed to be characterized by a specific frequency

of half heart rate: f

TWA

=0.5 cycles per beat, or f

TWA

1/(2*MRR) Hz. To account for physiological

variations of the RR interval, a narrow frequency

band, instead of a single frequency, was assumed

here to characterize the TWA phenomenon. On this

basis, our AMF was designed as a passband filter

with its passing band centred in f

TWA

. Technically,

the AMF was implemented as a 6

order

bidirectional Butterworth band-pass filter, having

the passing band 2·df

TWA

=0.12 Hz wide (value

experimentally found) and centred at a frequency

that adapts to mean RR interval. In particular, our

AMF was designed as a cascade of a low pass filter

(LPF) with cut-off frequency f

LPF

= f

TWA

+ df

TWA

, and

a high pass filter (HPF) with a cut-off frequency

HPF

= f

TWA

-df

TWA

. The squared module of the AMF

transfer function is expressed by the following

equation:

HPF

LPF

HPFLPFAMF

)w(H)w(H)w(H

222

⎟

⎠

⎞

⎜

⎝

⎛

⎟

⎠

⎞

⎜

⎝

⎛

⋅

⎟

⎠

⎞

⎜

⎝

⎛

=⋅=

(2)

IDENTIFICATION OF TIME-VARYING T-WAVE ALTERNANS FROM 20-MINUTE ECG RECORDINGS - Issues

Related to TWA Magnitude Threshold and Length of ECG Time Series

187

were n=3 (half of AMF order), w

LPF

=2πf

LPF

, and

HPF

=2πf

HPF

. Being the AMF applied in a

bidirectional fashion, no phase delay occurs. Thus,

the AMF is expected to detect the TWA signal by

filtering out not only noise and baseline wandering,

but also any other ECG component but the TWA.

The TWA signal provided by the AMF is a time

domain, constant phase and, possibly, amplitude-

modulated sinusoid with its maxima and minima

over the T-waves. A local estimate of TWA

amplitude (A

TWA

), associated to each single beat, is

directly given by the sinusoid amplitude in

correspondence of the T-wave apexes. If the T wave

of a beat is alternating, its A

TWA

is greater than zero.

In our procedure, all local A

TWA

values are used to

compute global (i.e. relative to all 128 beats of the

ECG series) estimates of TWA characteristic

parameters. In particular, the following global

parameters were determined: TWA duration

(TWAD, beat; defined as the total number of beats

with alternating T-waves), TWA amplitude (TWAA,

µV; defined as the mean A

TWA

over all alternating T-

waves), and TWA magnitude (TWAM, beat·µV;

defined as the product of TWAA times TWAD).

TWAM is used to detect the presence of TWA, since

it includes information about both TWAA and

TWAD. Moreover, TWAM allows identification of

different TWA episodes (such as those short in time

and high in amplitude, or long in time and low in

amplitude), which would not be detected if only

TWAD or TWAA, respectively, were used. Thus,

the AMF allows characterization of non-stationary

(i.e. time varying) characteristics of the TWA signal,

when present.

TWAD, TWAA and TWAM parameter values

are determined in each available lead.

Corresponding values from the three different leads

(X,Y,Z) are then averaged for final TWA

characterization relative to a specific 128-beat series.

The series with the highest TWAM is assumed as

the most representative of the entire 20-minute

recording.

2.3 Identification of TWA-Positive

Subjects

Considering that noise and artefacts may be detected

as TWA episodes, once TWA is identified and

parameterized, there is a need to define the TWA

level that characterizes a TWA-positive subject as

one at increased risk of sudden cardiac death. Taking

advantage of the H-group involved in our study, the

mean+2SD value of the TWAM distribution over

this group was assumed as the normality threshold

(THR

TWA

) of TWA magnitude. Thus, subjects with

TWAM greater than THR

TWA

were considered as

TWA positive (TWA+).

2.4 Statistical Analysis

Lilliefors test

(Lilliefors, 1967), was used to evaluate

the hypothesis that each data vector or parameter

vector had a normal distribution (significance was

set at 5% level) and could be expressed as mean ±

SD. Comparisons between two groups of normally

distributed samples were performed with two-tailed,

non-paired Student’s t-test (statistically significant

difference was assumed at P<0.05).

3 RESULTS

Application of our AMF method to an entire 20-

minute ECG recording (ECG20), with 128 beat ECG

series selected every 10 s, yielded normally

distributed TWA parameters with mean±SD for H-

group and AMI-group as given in Table 1. The

AMI-group was found to be characterized by having

significantly higher TWAD, TWAA, and TWAM.

The threshold value (THR

TWA

), as defined in

Methods, was 4176 beat·µV (that is, 2730+2×723).

With this threshold, fifteen patients of the AMI-

group (i.e. 43%) were classified as TWA+. No

subject of the H-group showed relevant TWA.

Extraction of ECG time series every 128 beats

(no overlap between two consecutive series)

provided a lower number of TWA+ among AMI-

patients (eleven cases, i.e. 31%), and significantly

lower estimates of TWA duration, amplitude and

magnitude parameters, with respect to the extraction

procedure performed every 10 s (Table 2).

Application of our AMF method to 128-beat

series taken in proximity of minutes 0 (

), 5 (t

), 10

(

), 15 (t

), and 20 (t

), yielded even lower

numbers of TWA+ patients associated with

significant reduction of mean TWAM, compared to

ECG20 with ECG time series extracted every 10 s

(Table 3). Eight (23%) out of the 15 (43%) patients

identified as TWA+ when analyzing ECG20

(namely, AMI02, AMI10, AMI11, AMI15, AMI18,

AMI19, AMI22 and AMI24), were never detected as

TWA+ when using single 128-beat series. Four

(11%) patients were detected as TWA+ at time t

and t

, two (6%) at t

, and only one (3%) at t

and

A representative example of the time course of

TWAD, TWAA and TWAM parameters averaged

over the three leads in our AMI01 patient is

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

188

displayed in Fig. 1a to c. Panel c clearly shows that

TWAM, which has been assumed as a marker of the

presence of TWA, crosses the THR

TWA

value at

different time instants. Under-threshold values of

TWAM are due to a simultaneous decrement of both

TWAD and TWAA (panels a and b).

Because TWAM shows fluctuations with

threshold-crossing within ECG20, TWA could even

be missed in all TWA+ subjects if our AMF was

applied to a single, short-term 128-beat ECG series.

Confirmation of this statement is found in Fig. 2,

where TWAM waves from all fifteen TWA+

patients are displayed. Arrow pointers in proximity

of the eleventh minute mark 128-beat ECG series

with under-threshold TWAM which would miss all

TWA+ cases.

4 DISCUSSION

To satisfy the requirement of heart-rate stability for

reliable TWA detection, short-term ECG series have

been considered for TWA identification in most

reported studies. Indeed, spectral analysis has been

the first technique proposed in the literature for

automatically detecting TWA (Adam et al., 1984).

ECG series of 128-consecutive beats were

considered for its application because this is the

minimal requirement to guarantee reliable spectral

analysis. Since then, ECG time series of 128-

consecutive beats have been traditionally used for

TWA quantification. Thus, the issue arises as to the

reliability of using a single 128 beat sequence to

detect TWA+ cases. The present study was

designed to address this issue by applying our AMF-

based method for TWA detection (Burattini et al.,

2006). Comparison was performed among the results

obtained from 128 beat ECG series selected 1) every

10 s (data overlap), 2) every 128 beats (no data

overlap), 3) every 5 minutes, in a time frame of 20

minutes. For this technical investigation, we

considered a population of 35 AMI-patients

compared with a population of 35 H-subjects. The

H-population was used as reference to define a

threshold (THR

TWA

) for TWAM parameter provided

by our method as a marker to identify a remarkable

level of TWA.

A novel finding of our analysis was that, based

on the defined threshold, the use of a unique 128

beat ECG series is unsuitable to unmask and detect

TWA. An explanation of this shortcoming is found

in that TWA is a transient phenomenon

characterized by time-varying TWAD, TWAA and

TWAM parameters (Fig. 1). As shown in Fig. 2,

under-threshold values of TWAM, assumed as

marker of TWA, would miss TWA+ patients if a

unique 128-beat ECG series in proximity of the

eleventh minute was used.

Table 1: Comparison between TWA duration (TWAD),

amplitude (TWAA), and magnitude (TWAM)

distributions (mean±SD) in the H-group and AMI-group.

Data refer to 20-minute ECG recordings (ECG20) with

128 beat time series extracted every 10 s.

H-group AMI-group t-test

TWAD

(beat)

75±13 87±11 P<0.01

TWAA

(µV)

43±14 56±22 P<0.01

TWAM

(beat

*µV)

2730±723 3982±1386 P<0.001

Table 2: Comparison between TWA duration (TWAD),

amplitude (TWAA), and magnitude (TWAM)

distributions (mean±SD) in AMI-group. Data refer to 20-

minute ECG recordings with 128 beat time series

extracted every 10 s (ECG20 overlap) or every 128 beats

(ECG20 no overlap).

AMI-

group

ECG20

overlap

ECG20

no overlap

t-test

TWA+ 15 11

TWAD

(beat)

87±11 81±11 P<0.001

TWAA

(µV)

56±22 50±21 P<0.001

TWAM

(beat

*µV)

3982±1386 3453±1253 P<0.001

Figure 1: TWA in the AMI01 patient. Panels a, b, and c:

respectively, TWA duration (TWAD), TWA amplitude

(TWAA), and TWA magnitude (TWAM) as functions of

time. In panel c the normality threshold is represented with

a dotted line.

IDENTIFICATION OF TIME-VARYING T-WAVE ALTERNANS FROM 20-MINUTE ECG RECORDINGS - Issues

Related to TWA Magnitude Threshold and Length of ECG Time Series

189

Figure 2: TWAM waves from our TWA+ patients. All them would not be recognised as TWA+ if a single 128 beat series

about the eleventh minute (arrows) was used, since TWAM is under threshold (dot line) about this time.

Table 3: TWA+ patients of AMI-group identified by our AMF method applied to the entire 20-minute ECG recording with

128 beats series extracted every 10 s (ECG 20 overlap), and to a single 128-beat series taken in proximity of minutes 0 (t

5 (t

), 10 (t

), 15 (t

), and 20 (t

). TWAM: TWA magnitude; TWA+: TWA positive patient. Student’s t-test is used to

compare the mean TWAM value over each considered 128-beat series (t

, t

, ..,t

) with mean TWAM over ECG 20.

AMI-group

ECG 20

overlap

TWA+

individual

patients

AMI01

AMI02

AMI03

AMI05

AMI10

AMI11

AMI15

AMI18

AMI19

AMI21

AMI22

AMI23

AMI24

AMI25

AMI26

AMI01

AMI03

AMI05

AMI21

AMI1

AMI23

AMI26

AMI01

AMI05

AMI25

AMI26

AMI05

Total TWA+

(43%)

(11%)

(3%)

(6%)

(11%)

(3%)

TWAM

(beat

*µV)

3982±1386 2708±1324 2352±974 2330±1030 2494±1298 2397±1178

t-test P<0.001 P<0.001 P<0.001 P<0.001 P<0.001

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

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We identified in 3-lead (X, Y, Z) 20-minute

digital ECG recordings a time frame that achieves a

good compromise between reliability of TWA

identification and computational efforts. Moreover, a

20-minute time frame is short enough to be possibly

obtained in controlled conditions, so that noise and

heart-rate variability due to emotional or physical

factors may not interfere significantly.

The definition of a threshold for TWAM is a

critical issue. The value identified here as mean

TWAM+2SD over our H-group yielded no presence

of TWA in this population, since the under-threshold

level of TWA is considered as background noise.

Our finding of no TWA in H-subjects is consistent

with what is commonly recognised in clinics.

Nevertheless, further studies on populations of

clinical relevance are desirable to define an optimal

normality threshold.

Several techniques have been proposed in the

literature for TWA detection (Adam et al., 1984;

Nearing et al., 1991; Burattini et al., 1999; Burattini

et al., 2006). Among these, the spectral method,

pioneered by Adam et al. (1984), is the most widely

used in clinics. However, being TWA a transient

(i.e. non-stationary) phenomenon (Kusmirek and

Gold, 2007; Cox et al., 2007; Richter et al., 2005;

and present study), a time-domain approach, as our

AMF method, appears, from a theoretical point of

view, more appropriate since it provides local (i.e.

relative to the single beat) as well as global (i.e.

relative to the entire ECG series under analysis)

TWA parameterization. Moreover, it is able to

discriminate between TWA phenomena sustained-

in-time (minutes) but low-in-amplitude and short-in-

time (few beats) but large-in-amplitude. Because

these two different kinds of TWA could potentially

have different clinical implications (statement to be

confirmed by future clinical studies), it appears

worthwhile to have a TWA detection method, which

allows discrimination between them. Such a

discrimination is not allowed by the spectral method,

which works, by definition, under the hypothesis of

stationary signal, and provides TWA measurements

that are averaged over the entire ECG time series

under analysis (128 beats). As a consequence, no

local (at the beat level) parameterization is possible

with the spectral method.

Two more TWA detection techniques proposed

in the literature are the correlation method (Burattini

et al., 1999) and the complex demodulation (Nearing

et al., 1991), which operate in the time domain.

Compared to the correlation method, our AMF

improves TWA detection in the presence of baseline

wanderings (Burattini et al., 2006). Complex

demodulation is computationally very heavy and has

never been used for practical purposes. In addition,

compared to any other TWA detection algorithm,

our AMF does not require pre-processing of the

ECG tracing, because noise and ECG frequency

components other than f

TWA

, are simultaneously

filtered out.

Our study suggests to analyse 20 minute ECG

recordings by applying our AMF to 128 beat ECG

time series selected every 10 seconds within a 20-

minute time frame. As a consequence, the TWA

global parameters (TWA duration, amplitude and

magnitude) associated to a time instant are the result

of an integration procedure over a 128 beats

window, corresponding (see Methods), on average,

to 119 s for the H-group, and 113 s for the AMI-

group. This, of course, results in a significant

overlap of data sets. A certain degree of overlap,

however, is necessary. In fact TWA episodes could

be divided into shorter ones during the windowing

procedure for 128 ECG time series extraction, and

this operation could prevent a correct TWA

detection and quantification. In addition, to be

eligible for TWA analysis, a 128 beat ECG is

required to satisfy the heart-rate stability condition

(eq. 1). The presence of local arrhythmic or noise

conditions, including ventricular premature beats,

artefacts, as well as false-positive and false-negative

beat detections, may cause rejection of a 128 beat

ECG. If no overlap among ECG time series is

present, all information on TWA in the time frame

belonging to the rejected ECG series is lost. Rather,

if a certain degree of overlap is allowed, some

information on TWA can be recovered from a close

ECG time series not affected by the local noise

factor. The time resolution recovering, consisting of

the transition from the global (relative to the entire

ECG series) domain to the local (relative to the

single beat) domain, is possible only with time-

domain TWA detection methods, through the

availability local TWA amplitude measure (A

TWA

Computational efforts limit the frequency of

time series extraction from an ECG20. Results of the

present study indicate that application of our AMF-

based method to 128 beat series extracted every 10 s

is a good compromise between reliability of non

stationary, transient TWA identification and

computational efforts. This kind of analysis, in fact,

can be routinely performed in real time in a clinic or

doctor’s office using a standard personal computer.

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