Abstract T wave alternans (TWA) is an electrophisiologic Introduction T-wave alternans is a prognostic indicator of preceding episodes of Torsade de Pointes life-threatening arrhythmia  TWA appears in the electrocardiogram as a consistent fluctuation in the repolarization morphology on everyother beat basis. The computerized ECG analysis led to the discovery, and routine clinical assessment, of invisible to the naked eye microvolt TWA  Heart rate alone appears to be the main factor of determining the onset of TWA during submaximal exercise stress tests  A variety of algorithms for detecting and quantifying TWA have been proposed, employing techniques as spectral analysis, complex demodulation, zero-crossings counting in a series of correlation coefficients, KarhunenLoève transform, low-pass Capon filtering, Poincaré mapping, periodicity transforms, statistical tests, modified moving average, Laplacian likelihood ratio, etc. A review by Martínez and Olmos [8]  highlights the need for methodological systematization effort in characterization and comparison of the different methods. A multilead approach to T-wave alternans detection combining PCA and the Laplacian likelihood ratio method is proposed by Monasterio and Martínez  The aim of this study is to assess the detection and quantification of TWA, by the performance of two combined methods: Twave amplitude statistical analysis and PCA in the framework of PhysioNet/Computers in Cardiology 2008 Challenge [10]. Methods A set of 100 ECG recordings were selected and collected in the framework of the Challenge [10] in order to test different algorithms for the detection and the quantification of TWA. These recordings consisted of 16 with 2 leads, 12 with 3 leads and the remaining 72 recordings with 12 leads

G Bortolan

I I Christov

CiteSeerX

Principal Component Analysis for Detection 
and Assessment of T-Wave Alternans 
 
G Bortolan
1
, II Christov
2
 
 
1
Institute of Biomedical Enginnnering, ISIB - CNR, Padova, Italy 
2
Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria 
Abstract 
T wave alternans (TWA) is an electrophisiologic 
phenomenon associated with a risk factor of sudden 
cardiac death. In the framework of Physionet/Cinc 
Challenge, a set of 100 ECG recordings were collected in 
order to test different algorithms for the detection and the 
quantification of TWA. 
The ECG were processed for power line interference, 
electromyographic noise and baseline wander 
suppression. Two kind of algorithm were tested, 
considering the temporal domain: 1) the T wave 
amplitude computed in a combined lead, and 2) the 
Principal Component Analysis for quantifying the 
complexity index of the T waves. The detection of possible 
alternans was performed by a paired non parametric 
statistical test. Our study proved a high correlation 
between alternans of T amplitude and the alternans of T 
wave complexity index. The results of an algorithm 
combining the outcome of the two methods for the 
detection of TWA showed a score of 0.890, second best in 
the Challenge. 
 
1. Introduction 
 
T-wave alternans is a prognostic indicator of preceding 
episodes of Torsade de Pointes life-threatening 
arrhythmia [1], and is an electrophysiological 
phenomenon associated with a risk factor of sudden 
cardiac death. Linkage between TWA and susceptibility 
to malignant arrhythmias has been reported at above 75% 
of the controls (p <.05) [2]. 
TWA appears in the electrocardiogram as a consistent 
fluctuation in the repolarization morphology on every-
other beat basis. The computerized ECG analysis led to 
the discovery, and routine clinical assessment, of 
invisible to the naked eye microvolt TWA [3-5]. TWA 
appears at a specific heart rate, generally in the range of 
90 to 120 beats per minute, and can be achieved using 
bicycle or treadmill stress test. Both exercises have 
almost equal effect [6].  
Heart rate alone appears to be the main factor of 
determining the onset of TWA during submaximal 
exercise stress tests [6]. 
Weber et al. [7] investigated the prevalence of TWA 
in healthy subjects. TWA was not observed in any 
individual at rest. Short transient intervals of TWA were 
observed during exercise in 10.4% of the healthy controls 
(30 ± 8 years). Sustained TWA was observed in 4.2%.  
A variety of algorithms for detecting and quantifying 
TWA have been proposed, employing techniques as 
spectral analysis, complex demodulation, zero-crossings 
counting in a series of correlation coefficients, Karhunen-
Loève transform, low-pass Capon filtering, Poincaré 
mapping, periodicity transforms, statistical tests, 
modified moving average, Laplacian likelihood ratio, etc. 
A review by Martínez and Olmos [8] highlights the 
need for methodological systematization effort in 
characterization and comparison of the different methods. 
A multilead approach to T-wave alternans detection 
combining PCA and the Laplacian likelihood ratio 
method is proposed by Monasterio and Martínez [9]. 
The aim of this study is to assess the detection and 
quantification of TWA, by the performance of two 
combined methods: Twave amplitude statistical analysis 
and PCA in the framework of PhysioNet/Computers in 
Cardiology 2008 Challenge [10].  
 
2. Methods 
 
A set of 100 ECG recordings were selected and 
collected in the framework of the Challenge [10] in order 
to test different algorithms for the detection and the 
quantification of TWA. These recordings consisted of 16 
with 2 leads, 12 with 3 leads and the remaining 72 
recordings with 12 leads.  
 
2.1. Signal preprocessing 
 
Moving averaging of samples in one period of power-
line interference was performed. This filter is meant to 
eliminate the power-line interference. Its frequency 
response is having a first zero at interference frequency 
50 Hz (60 Hz). 
ISSN 0276−6574 521 Computers in Cardiology 2008;35:521−524.
 
 
A smoothing procedure for EMG noise suppression is 
applied [11]. It uses the least-squares approximation 
method, applied for defining the weighting coefficients 
for each sample of the selected smoothing interval of 60 
ms. 
A high-pass recursive filter for drift suppression with a 
cutoff frequency of 0.64 Hz has been used [12]. 
QRS detection using combined adaptive thresholding 
was applied [13]. 
T wave onset and offset delineations were 
automatically performed [14], and the T amplitude was 
calculated, in a combined lead simulating the spatial 
vector [12] or another all-inclusive signal, rather than in 
the separate leads. The transform to the orthogonal leads 
(X,Y,Z) was performed in [12] using ‘primary leads’, i.e. 
the 8 potential differences referred to the electrode of the 
left leg F. These primary leads were obtained from the 
12-lead ECG recordings, following the conversion 
formulae in the [15]: 
RF = -II;    LF = -III;    CiF = Vi – (II+III)/3 
The orthogonal leads were evaluated by: 
 X=0.5*abs(C4F-C1F); 
 Y=abs(RF); 
 Z=abs(RF -C2F); 
The combined lead (CL), which is the spatial vector in 
this case, is calculated by: 
CL=0.5(X+Y+Z+0.25(abs(X-Y)+abs(X-Z)+abs(Y-Z)));  
In cases of 3-leads ECG: X=Lead1; Y=Lead2; 
Z=Lead3. 
In cases of 2-leads ECG: X=Lead1; Y=Lead2; Z=0. 
 
2.2. TWA detection 
 
The proposed approach takes into consideration three 
aspects of the TWA detection task: the parameter 
selection, the interval selection and the classification 
bock. 
In selecting the more appropriate parameter for TWA 
classification, the multi-lead approach has been followed, 
in order to extract a single index from the entire ECG 
record. Two parameters were chosen, considering the 
temporal domain: 1) the amplitude and 2) the complexity 
index of T waves. In the first case, a combined lead was 
determined with all the leads at disposal (2, 3 or 12), 
simulating the spatial vector, in which T wave onset and 
offset delineations  were performed, and  the amplitudes 
were computed. 
The second parameter for TWA discrimination 
considers the use of Principal Component Analysis 
(PCA) for quantifying the complexity index of the T 
waves. In particular the PCA has been applied to the 
intervals of T waves, and the ratio of 2nd/1st eigenvalues 
characterizes the complexity index. 
In the interval selection two methods were applied: 
global and local (see Figure 1). In the global method, the 
entire ECG recording was processed, producing a unique 
time series, which feed the detection block. The local 
method considers a set of variable length windows of RR 
intervals, and performing the parameter extraction in each 
of them. In particular this method considers a window of 
128 RR intervals, shifting it in the entire ECG recording.  
The detection block performs first, the separation of 
the parameters from odd and even RR intervals, and then 
the two series was compared in order to discover the 
presence of significative differences. The statistical 
analysis which provides the significance of the 
differences between odd and even samples was 
performed by the non parametric paired-sampled 
Wilcoxon signed rank test. 
In the case of Global methods, the statistical test 
produces a single binary index, which represents the 
presence or absence of TWA. In the case of Local 
methods, this process is repeated for every interval, 
producing a set of binary indices, and the number of 
intervals with positive indices is considered. In case it is 
higher/lower of a predetermined threshold, it signifies the 
presence/absence of TWA.  
 
 
 
 
Fig.1. Methods for the interval selection for the 
detection of TWA: global (entire record) and local (a set 
of windows) 
 
 
All the RR intervals were analyzed, independently by 
the presence of noise or artifact. In addition, the heart rate 
was not considered for the determination of the 
presence/absence of TWA. 
 
3. Results 
 
The Computers in Cardiology Challenge made 
available a score system in order to rank the various 
methods.  The score takes values in the range -1, +1. 
Global methods  
Local methods   
522
 
 
Five methods have been tested for the detection of 
TWA: 
- GLB1: a global index considering the alternans of the 
T amplitude on the entire ECG recording 
- GLB2: a global index considering the PCA index on 
the entire recording 
- LOC1: a local index considering the alternans of the 
T amplitude in various windows of 128 RR intervals 
- LOC2: a local index considering the alternans of the 
PCA index in the individual intervals 
- COMB: an index combining the previous indices, 
and requiring essentially a positive test in both the global 
and the local in dices. 
Table 1 reports a cross tabulation of the number of 
records with the presence of TWA with the 5 considered 
methods.  
Table 2 reports the percentage of agreement in the 
detection results between all the combinations of two 
methods, while Table 3 reports the correlation coefficient 
between them. 
Table 1. Number of TWA determined by the 5 methods: 
Comb (Combined), GLB1 (Global T amplitude), .GLB2 
(Global PCA_T), LOC1 (Local T amplitude), and LOC2 
(Local PCA_T). 
 Comb GLB1 GLB2 LOC1 LOC2 
Comb 25     
GLB1 25 29    
GLB2 20 20 28   
LOC1 23 27 18 31  
LOC2 19 19 20 17 24 
 
Table 2. Agreement between the 5 different methods  of 
TWA detection: Comb (Combined), GLB1 (Global T 
amplitude), .GLB2 (Global PCA_T), LOC1 (Local T 
amplitude), and LOC2 (Local PCA_T). 
 Comb GLB1 GLB2 LOC1 LOC2 
Comb 100%     
GLB1 96% 100%    
GLB2 87% 83% 100%   
LOC1 90% 94% 77% 100%  
LOC2 89% 85% 88% 79% 100% 
 
Our study proved a high correlation between alternans 
of T amplitude (GLB1 and LOC1) and the alternans of T 
wave complexity index (GLB2 and LOC2). For example 
the two indices were in agreement in 83% and 79% in the 
global and local detection respectively. 
Table 4 shows the Challenge scores of the 5 methods. 
The Combined method reach the highest score, 0.890, 
which is the second best in the Challenge. It turns out that 
all the tested methods reach a higher score than 0.800, 
which is a satisfiable performance. In addition, in the 
 
Table 3. Correlation coefficient between the 5 different 
methods  of TWA detection: Comb (Combined), GLB1 
(Global T amplitude), .GLB2 (Global PCA_T), LOC1 
(Local T amplitude), and LOC2 (Local PCA_T). 
 Comb GLB1 GLB2 LOC1 LOC2 
Comb 1.00     
GLB1 0.90 1.00    
GLB2 0.67 0.58 1.00   
LOC1 0.70 0.80 0.40 1.00  
LOC2 0.66 0.56 0.63 0.46 1.00 
 
 
Table 4. Scores of  the 5 methods  of TWA detection: 
Comb (Combined), GLB1 (Global T amplitude), .GLB2 
(Global PCA_T), LOC1 (Local T amplitude), and LOC2 
(Local PCA_T). 
Method Score 
  
Comb 0.890 
GLB1 0.861 
GLB2 0.802 
LOC1 0.831 
LOC2 0.812 
 
 
Table 5.List of records with positive TWA detection with 
the Combined (Comb) method. 
twa01 twa32 twa73 
twa06 twa33 twa79 
twa09 twa34 twa82 
twa13 twa50 twa88 
twa15 twa51 twa91 
twa17 twa64 twa97 
twa21 twa67 twa98 
twa25 twa69  
twa29 twa72  
 
 
40 60 80 100 120 140
0
5
10
15
20
25
 
 
Heart Rate  
Figure 2. Histogram of heart rate in patients with (blue 
bars) and without (red bars) the presence of TWA 
determined by the Combined (Comb) method. 
523
 
 
case of T amplitude, the global method achieves a better 
result than the local method, while the contrary is true for 
the PCA index. Table 5 reports the list of all the ECG 
records with the presence of TWA considering the 
Combined method, and Figure 2 shows the histogram of 
the heart rate. 
 
4. Discussion and conclusions 
 
The task of TWA detection has been studied 
considering two parameters in the time domain: the T 
wave amplitude computed in a combined lead, and the 
complexity index of the T waves. The classification has 
been performed analyzing the entire ECG records or a set 
of intervals. The results show that the combined 
algorithm has the best performance, as it could be 
expected in general. The parameters considering the T 
amplitude have a higher performance compared to the T 
wave complexity index based on PCA. However, the two 
parameters showed a good agreement between them, and 
there was less score differences in the local methods.  
 
Acknowledgements 
 
The authors are greatly indebted for personal com-
munications about the physiological aspects of TWA to: 
Velislav Batchvarov, MD, PhD, Department of 
Cardiac and Vascular Sciences, St. George’s University 
of London, London, United Kingdom 
Iana Simova, MD, Clinic of Cardiology, University 
Hospital ‘Aleksandrovska’, Medical University, Sofia, 
Bulgaria  
 
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Address for correspondence 
 
Giovanni Bortolan 
ISIB-CNR 
Corso Stati Uniti, 4 
35129 Padova , Italy 
bortolan@isib.cnr.it  
 
524


Principal component analysis for the detection and assessment of T-wave alternans

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Principal component analysis for the detection and assessment of T-wave alternans

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