Signal-averaged P wave analysis for delineation of interatrial conduction – Further validation of the method

Abstract

The study was designed to investigate the effect of different measuring methodologies on the estimation of P wave duration. The recording length required to ensure reproducibility in unfiltered, signal-averaged P wave analysis was also investigated. An algorithm for automated classification was designed and its reproducibility of manual P wave morphology classification investigated.
Twelve-lead ECG recordings (1 kHz sampling frequency, 0.625 microV resolution) from 131 healthy subjects were used. Orthogonal leads were derived using the inverse Dower transform. Magnification (100 times), baseline filtering (0.5 Hz high-pass and 50 Hz bandstop filters), signal averaging (10 seconds) and bandpass filtering (40-250 Hz) were used to investigate the effect of methodology on the estimated P wave duration. Unfiltered, signal averaged P wave analysis was performed to determine the required recording length (6 minutes to 10 s) and the reproducibility of the P wave morphology classification procedure. Manual classification was carried out by two experts on two separate occasions each. The performance of the automated classification algorithm was evaluated using the joint decision of the two experts (i.e., the consensus of the two experts).
The estimate of the P wave duration increased in each step as a result of magnification, baseline filtering and averaging (100 +/- 18 vs. 131 +/- 12 ms; P < 0.0001). The estimate of the duration of the bandpass-filtered P wave was dependent on the noise cut-off value: 119 +/- 15 ms (0.2 microV), 138 +/- 13 ms (0.1 microV) and 143 +/- 18 ms (0.05 microV). (P = 0.01 for all comparisons). The mean errors associated with the P wave morphology parameters were comparable in all segments analysed regardless of recording length (95% limits of agreement within 0 +/- 20% (mean +/- SD)). The results of the 6-min analyses were comparable to those obtained at the other recording lengths (6 min to 10 s). The intra-rater classification reproducibility was 96%, while the interrater reproducibility was 94%. The automated classification algorithm agreed with the manual classification in 90% of the cases.
The methodology used has profound effects on the estimation of P wave duration, and the method used must therefore be validated before any inferences can be made about P wave duration. This has implications in the interpretation of multiple studies where P wave duration is assessed, and conclusions with respect to normal values are drawn.P wave morphology and duration assessed using unfiltered, signal-averaged P wave analysis have high reproducibility, which is unaffected by the length of the recording. In the present study, the performance of the proposed automated classification algorithm, providing total reproducibility, showed excellent agreement with manually defined P wave morphologies.

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BMC Cardiovascular Disorders
Open Access
Research article
Signal-averaged P wave analysis for delineation of interatrial
conduction – Further validation of the method
Fredrik Holmqvist, Pyotr G Platonov*, Rasmus Havmöller and Jonas Carlson
Address: Department of Cardiology, Lund University Hospital, SE 221 85, Lund, Sweden
Email: Fredrik Holmqvist - fredrik.holmqvist@med.lu.se; Pyotr G Platonov* - pyotr.platonov@med.lu.se;
Rasmus Havmöller - rasmus.havmoller@med.lu.se; Jonas Carlson - jonas.carlson@med.lu.se
* Corresponding author
Abstract
Background: The study was designed to investigate the effect of different measuring methodologies on the estimation of P
wave duration. The recording length required to ensure reproducibility in unfiltered, signal-averaged P wave analysis was also
investigated. An algorithm for automated classification was designed and its reproducibility of manual P wave morphology
classification investigated.
Methods: Twelve-lead ECG recordings (1 kHz sampling frequency, 0.625
µ
V resolution) from 131 healthy subjects were used.
Orthogonal leads were derived using the inverse Dower transform. Magnification (100 times), baseline filtering (0.5 Hz high-
pass and 50 Hz bandstop filters), signal averaging (10 seconds) and bandpass filtering (40–250 Hz) were used to investigate the
effect of methodology on the estimated P wave duration. Unfiltered, signal averaged P wave analysis was performed to determine
the required recording length (6 minutes to 10 s) and the reproducibility of the P wave morphology classification procedure.
Manual classification was carried out by two experts on two separate occasions each. The performance of the automated
classification algorithm was evaluated using the joint decision of the two experts (i.e., the consensus of the two experts).
Results: The estimate of the P wave duration increased in each step as a result of magnification, baseline filtering and averaging
(100 ± 18 vs. 131 ± 12 ms; P < 0.0001). The estimate of the duration of the bandpass-filtered P wave was dependent on the
noise cut-off value: 119 ± 15 ms (0.2
µ
V), 138 ± 13 ms (0.1
µ
V) and 143 ± 18 ms (0.05
µ
V). (P = 0.01 for all comparisons).
The mean errors associated with the P wave morphology parameters were comparable in all segments analysed regardless of
recording length (95% limits of agreement within 0 ± 20% (mean ± SD)). The results of the 6-min analyses were comparable to
those obtained at the other recording lengths (6 min to 10 s).
The intra-rater classification reproducibility was 96%, while the interrater reproducibility was 94%. The automated classification
algorithm agreed with the manual classification in 90% of the cases.
Conclusion: The methodology used has profound effects on the estimation of P wave duration, and the method used must
therefore be validated before any inferences can be made about P wave duration. This has implications in the interpretation of
multiple studies where P wave duration is assessed, and conclusions with respect to normal values are drawn.
P wave morphology and duration assessed using unfiltered, signal-averaged P wave analysis have high reproducibility, which is
unaffected by the length of the recording. In the present study, the performance of the proposed automated classification
algorithm, providing total reproducibility, showed excellent agreement with manually defined P wave morphologies.
Published: 9 October 2007
BMC Cardiovascular Disorders 2007, 7:29 doi:10.1186/1471-2261-7-29
Received: 8 February 2007
Accepted: 9 October 2007
This article is available from: http://www.biomedcentral.com/1471-2261/7/29
© 2007 Holmqvist et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0
),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Background
Studies on P wave duration and morphology are common
in the literature [1-7]. However, the method use for the
estimation of P wave duration varies widely [1-7]. As can
be expected, the reported estimates of P wave duration
vary depending on the underlying heart disease [2-5,7],
but more surprisingly, substantial differences between
similar study populations can also be found [2,5]. This
implies that not only the underlying heart disease but also
the methodology may affect the estimation of the P wave
duration. To improve comparability between studies, the
effects of measurement method on P wave analysis must
be investigated. At present, the exact cut-off value defining
interatrial conduction delay (presented as a prolongation
of the P wave) [8,9] is the subject of much debate [1,6].
However, little attention is paid to the method used.
Studies using signal-averaged P wave analysis usually also
employ bandpass filtering. However, 'unfiltered' signal-
averaged P wave analysis (i.e. signal averaging without
bandpass filtering) [10] has been shown to reveal differ-
ences in P wave morphology between patients with a high
prevalence of [2] or propensity for [7] atrial fibrillation
and their healthy counterparts. Three different P wave
classes (Figure 1), possibly indicating differences in inter-
atrial conduction have been identified [7]. Classification
has been performed manually without strictly formalised,
objective criteria.
In the present study, the influence of methodology on the
estimation of P wave duration was investigated. The
requirements for reproducibility using unfiltered, signal-
averaged P wave analysis were also investigated, focusing
on recording length and automated classification.
Methods
Definitions
Agreement is defined as identical results within the accu-
racy of the measurements. Limits of agreement are used as
defined by Bland and Altman [11]. Reproducibility is the
term used to express numerical equivalence between
Different types of P wave morphologyFigure 1
Different types of P wave morphology. Typical examples of: Type 1 P wave morphology (A), Type 2 P wave morphology
(B) and Type 3 P wave morphology (C). Type 3 morphology has previously been shown to be compatible with Bachmann's
bundle block [20,21].
ABC
−50 0 50 100 150
0
20
40
Lead X
−50 0 50 100 150
0
20
40
Lead Y
−50 0 50 100 150
−40
−20
0
Lead Z
−50 0 50 100 150
0
20
40
60
[ms]
SM
−50 0 50 100 150
−20
0
20
40
60
80
−50 0 50 100 150
−20
0
20
40
60
80
−50 0 50 100 150
−50
0
50
−50 0 50 100 150
0
50
100
[ms]
0 50 100 150 200
−50
0
50
0 50 100 150 200
−50
0
50
0 50 100 150 200
−50
0
50
0 50 100 150 200
0
20
40
60
[ms]

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results. Total reproducibility refers only to the perform-
ance of the automated algorithm.
Study population and data acquisition
A data set containing ECG data from 131 healthy subjects
(denoted the 'evaluation set', mean age 51 ± years, 54%
women) without a history of heart disease, was used to
investigate the methodological effects on P wave duration
and morphological parameters. The same data set was
also used to evaluate the automatic P wave classification
algorithm.
Another data set, containing ECG data from 107 subjects
with a history of heart disease (i.e. atrial fibrillation or
hypertrophic cardiomyopathy) was used as a training set
for the automated classification algorithm (denoted the
'training set').
The study was approved by the Ethics Committee of Lund
University (approval number LU 325-00). Written
informed consent was obtained and the study complied
with the Declaration of Helsinki. In both data sets, six
minutes of 12-lead ECG data had been acquired (1 kHz
sampling, 16-bit A/D-conversion, 0.625
µ
V resolution)
using a custom-made, optically isolated PC card (Siemens
Elema AB, Solna, Sweden). The data were transferred to a
computer and stored for subsequent off-line processing.
To enable the analysis of orthogonal P wave morphology,
a vectorcardiogram (VCG) was derived from the 12-lead
ECG data using the inverse Dower transform [12,13].
Methods of estimating P wave duration
A subset of the database (48 recordings, representative of
the entire database of healthy subjects, in terms of age and
gender distribution) was used for this part of the study. P
wave duration was manually determined using a dedi-
cated computer program running under MATLAB R14
(The MathWorks Inc., Natick, MA, USA).
Non-magnified ECGs were investigated at 50 mm/s and
10 mV/mm resolution, magnified ECGs at 1000 mm/s
and 0.1 mV/mm. Artefact filtering was applied using high-
pass filtering (0.5 Hz) to exclude slow baseline drift due
to respiratory movement of the thorax, and a 50 Hz band-
stop filter to reduce power line interference. Signal averag-
ing (described below) and bandpass filtering (40–250
Hz), with varying noise level cut-off (0.05
µ
V to 0.20
µ
V)
were applied in the final stages. The combinations of the
parameters used (denoted D1 to D8) are summarised in
Table 1.
Signal averaging of P waves
The derived VCG was high-pass filtered (0.5 Hz) to
exclude slow baseline drift due to respiratory movement
of the thorax. Power line interference was reduced using a
50 Hz bandstop filter. QRS complexes were identified
automatically and included according to similarity (a
cross-correlation coefficient of
ρ
> 0.9 was applied in
order to exclude artefacts and abnormal events such as
extra ventricular beats). P waves were extracted using 250-
ms signal windows preceding each QRS complex. In cases
of unusually long PQ time or P wave duration, the win-
dow could be shifted manually in order to fully cover the
P wave. Subsequently, the signal windows were time
shifted to estimate the maximal correlation in each lead. P
waves with a cross-correlation coefficient of
ρ
> 0.9 (in
each lead) were grouped together and averaged. The onset
and end of the signal-averaged P waves were defined man-
ually. The amplitude at onset was set to 0 V. The method
used is described in detail elsewhere [10]. The parameters
used to quantify P wave morphology are illustrated sche-
matically in Figure 2.
P wave morphology classification
P waves were classified into three different types based on
their morphology: Type 1 (predominantly positive Leads
X and Y and predominantly negative Lead Z), Type 2 (pre-
dominantly positive Leads X and Y and biphasic Lead Z
(negative, positive) and Type 3 predominantly positive
Lead X and biphasic Leads Y (positive, negative) and Z
(negative, positive). P waves not classifiable according to
these three types were denoted 'Atypical'.
Table 1: P wave duration estimation methodologies
Magnification Artefact filtering Signal averaging Bandpass filtering Noise threshold
D1 no no no no -
D2 no yes no no -
D3 yes no no no -
D4 yes yes no no -
D5 yes yes yes no -
D6 yes yes yes yes 0.20
µ
V
D7 yes yes yes yes 0.10
µ
V
D8 yes yes yes yes 0.05
µ
V
Non-magnified ECG (50 mm/s, 10 mV/mm), magnified ECG (1000 mm/s; 0.1 mV/mm). Artefact filtering: high-pass filtering at 0.5 Hz and 50 Hz
bandstop filtering. Bandpass filtering: 40–250 Hz. The P wave duration was determined using three different noise level thresholds.

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Manual classification
Manual P wave morphology classification was performed
manually and independently by two expert operators.
Each expert classified all P wave morphologies on two
occasions, separated by at least three days to estimate
intra-rater variability. P wave morphologies assigned dif-
ferent classifications on the first and second occasions
were classified on a third occasion.
The classifications of the two experts were then compared
to estimate interrater variability. P wave morphologies
classified differently by the two experts were finally classi-
fied based on a joint decision of the two experts. This
"final manual classification" was used when evaluating
the automated classification.
Automated classification
In order to force the P wave amplitude to 0 V at the end,
linear interpolation was applied from the onset to the end
of the wave in each lead and the linear segment was sub-
tracted. The positions and amplitudes of the largest maxi-
mum and smallest minimum in each lead and the
location of the zero-crossings were calculated. If the max-
imum or minimum had an amplitude less than one fifth
of the P wave amplitude it was discarded by the algorithm,
thus not influencing the results.
Each lead was described by a three-element vector, {* ; *
; *}. Position one describes the first maximum or mini-
mum (if any) of the P wave (1, -1 or 0). If at least one zero-
crossing was located in the mid third of the P wave, posi-
tion 2 was given the value 1, if not it was given the value
0. Position three describes the second maximum or mini-
mum (if any) of the P wave (1, -1 or 0). The classification
of the three different types of P waves and their corre-
sponding vectors are summarised in Table 2. The perform-
ance of the automated classification algorithm was
evaluated by comparing the results with those of the final
manual classification.
Material
Both the data sets (evaluation and training sets) were
manually classified as described above. The automated
classification algorithm parameters were optimised using
the training set and the performance was then evaluated
using the evaluation set.
Recording length
A subset of the database (48 recordings) was used for this
part of the study. Data were analysed with respect to
standard P wave morphology parameters using 6-min
(L1), 3-min (L2), 1-min (L3), 30-s (L4) and 10-s (L5) seg-
ments. In an additional analysis (L6) a 10-s segment with
a lower signal resolution (sampling frequency 500 Hz,
sampling resolution 5
µ
V) was applied. The P wave mor-
phology parameters from (L1-L6) were compared with a
Table 2: Automated classification algorithm
Type 1 Type 2 Type 3
Lead X {1 ; * ; 0} {1 ; * ; 0} {1 ; * ; 0}
{1 ; 0 ; *} {1 ; 0 ; *} {1 ; 0 ; *}
{0 ; * ; 1} {0 ; * ; 1} {0 ; * ; 1}
Lead Y {1 ; * ; 0} {1 ; * ; 0}
{1 ; 0 ; *} {1 ; 0 ; *} {1 ; 1 ; -1}
{0 ; * ; 1} {0 ; * ; 1}
Lead Z {-1 ; * ; 0}
{-1 ; 0 ; *} {-1 ; 1 ; 1} {-1 ; 1 ; 1}
{0 ; * ; -1}
Position one refers to the first peak detected and position three to
the second peak (1 = maximum; -1 = minimum; 0 = neither). Position
two refers to zero-crossings in the mid third of the P wave (1 =
present; 0 = absent). A * indicates that any value is acceptable.
Measured parametersFigure 2
Measured parameters. Schematic illustration of the
parameters derived from signal-averaged P wave morpholo-
gies. Lead X: location (A) and amplitude (B) of maxima; Lead
Y: location (C) and amplitude (D) of maxima; Lead Z: loca-
tion (E) and amplitude (F) of minimum, location of zero-
crossing (G), location (H) and amplitude (I) of maximum.
A
B
Lead X
C
D
Lead Y
E G H
F
I
Lead Z

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baseline 6-min analysis (as described above) to assess the
agreement between the clinical measurements.
Statistical analysis
Data are presented as the mean and the standard devia-
tion. Paired data were compared using the Wilcoxon
matched-pairs test. The chi-squared test was used to eval-
uate the relationship between dichotomous variables. Sta-
tistical significance was defined as P < 0.05. The Bland-
Altman method [11]was used to assess the agreement
between samples. All statistical analyses were performed
using STATISTICA for Windows version 6.1 (StatSoft, Inc.,
Tulsa, OK, USA).
Results
P wave duration
The P wave durations measured using the first five combi-
nation of parameters were 100 ± 18 ms (D1), 99 ± 16 ms
(D2), 115 ± 17 ms (D3), 125 ± 13 ms (D4) and 131 ± 12
ms (D5). The differences were significant (P < 0.0001) for
all comparisons with the exception of D1 vs. D2 (P = 0.7).
These results are illustrated in Figure 3.
The P wave durations in parameter combinations D6, D7
and D8 were 119 ± 15 ms, 138 ± 13 ms, and 143 ± 18 ms,
respectively. These differences were also significant (P <
0.01 for all comparisons). Moreover, the estimates of P
wave duration using these parameter combinations were
all significantly different from the estimate obtained with
D5 (P < 0.01 for all comparisons).
P wave morphology classification
The intra-rater agreement was 96% for both experts while
the interrater agreement was 94%. The final manual clas-
sification of the evaluation set, resulted in the following
distribution of P wave morphologies: 35/63/0/2 % for
Type 1, Type 2, Type 3 and Atypical, respectively.
The optimised classification criteria resulted in correct
classification in 97 of the 107 subjects in the training set
(91%). The automated classification by the algorithm
agreed in 90% of the cases with the final manual classifi-
cation of the evaluation set. The distribution of P wave
morphologies was 39/59/1/2 % for Type 1, Type 2, Type
3 and Atypical, respectively. The difference in distribution
of Types 1 and 2 was not statistically significant compared
with the final manual classification (P = 0.56). Differences
in the distribution of Type 3 and Atypical were not ana-
lysed due to the small numbers.
P wave duration and methodologyFigure 3
P wave duration and methodology. Estimates of average P wave duration in the study population using different method-
ologies. The various combinations of parameters (D1 to D8) are summarised in Table 1. The three shaded boxes (D6-D8) rep-
resent estimates from bandpass-filtered analysis.

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Recording length
The mean error in all parameters was similar in all the seg-
ments analysed regardless of recording length (95% limits
of agreement within 0 ± 20% for all parameters and seg-
ments). Individual parameters and their corresponding
limits of agreement are listed in Table 3. The results of the
P wave duration analyses are illustrated in Figure 4.
The results of the 6-min analyses (L1) were comparable to
those with shorter recording lengths (L2-L5). The analysis
revealed no considerable error resulting from lower signal
resolution (L6) (Table 3). The performance of the auto-
mated classification algorithm was thus not affected by
the length of the recording, showing only occasional
changes in classification between the different recording
lengths (Table 3).
Discussion
P wave duration
The methods used for P wave analysis vary greatly, as do
the reported P wave durations in various patient popula-
tions [3-5]. The present study is, to the best of our knowl-
edge, the first attempt to compare these different
methodologies. The marked differences in P wave dura-
tion resulting from the method used underscores the need
for method validation and uniformity in order to increase
comparability between studies. The ongoing debate on
the appropriate cut-off value for interatrial conduction
delay [1,6], seems to be inappropriate bearing in mind the
results of the current study.
Few invasive studies report average total atrial activation
time, but when reported it has been shown to be
approaching 120 ms [14,15]. In other invasive studies
where right and/or left atrial activation time are reported
separately, these commonly exceed 65 to 75 ms [16-18].
There is thus evidence that the 'true' P wave duration on
average exceeds 120 ms in invasive measurements in a
wide variety of study populations. Moreover, in one study
reporting invasively measured biatrial activation time, as
well as P wave duration estimated from conventional
ECG, the former was 20% longer than the latter [14]. All
these findings imply that the longer estimates of P wave
duration, observed in the present study when magnifica-
tion, filtering and averaging are applied, are the result of
better delineation of the P wave as the noise level
decreases. Interestingly, when 15 ms was added to the
widely accepted cut-off value for interatrial conduction
delay (120 ms), the prevalence of interatrial block in the
entire study population was equal to that expected based
on population age distribution [19].
The estimates of P wave duration obtained when using the
widespread technique of bandpass filtering of the signal-
averaged P waves, varied considerably depending on the
noise cut-off value chosen (Figure 5). This has important
implications when comparing results from different stud-
ies applying this technique.
P wave morphology classification
Based on findings in invasive studies it is reasonable to
assume that the right and left atrial activation times are
about the same [16-18]. It is therefore logical to assume
that the first and the last third of the P wave primarily rep-
resents right and left atrial activation respectively, whereas
the middle third is likely to represent electrical activity
from both atria and interatrial septum. This assumption is
supported by invasive studies [14]. Therefore, although
the cut-off value was arbitrarily chosen to optimise the
performance in the training set, the cut-off values used in
the classification algorithm are based on scientific find-
ings.
Table 3: Recording length
6 min 3 min 1 min 30 s 10 s 10 s LR
P dur 2.9 ± 18 -3.7 ± 15 1.9 ± 16 3.2 ± 17 1.4 ± 19 1.0 ± 18
Xmax pos 0.1 ± 4.7 -1.0 ± 5.8 0.6 ± 15 0.7 ± 13 0.8 ± 18 1.2 ± 18
Xmax amp 0.0 ± 0.4 0.0 ± 1.9 -0.1 ± 4.2 -0.2 ± 4.5 0.1 ± 5.6 -0.1 ± 5.7
Ymax pos 0.1 ± 4.8 -1.5 ± 8.6 -1.0 ± 7.7 -0.6 ± 10 -1.4 ± 11 -0.9 ± 11
Ymax amp 0.0 ± 0.6 -0.4 ± 4.7 -1.3 ± 13 -0.8 ± 14 0.2 ± 18 0.0 ± 17
Zmin pos 0.1 ± 4.7 -0.9 ± 5.7 0.6 ± 9.3 -0.2 ± 9.0 -1.6 ± 12 -1.1 ± 11
Zmin amp 0.1 ± 0.7 -0.1 ± 2.0 0.4 ± 5.0 0.3 ± 4.1 0.7 ± 6.1 0.9 ± 6.2
Zzero pos 0.0 ± 0.4 -1.0 ± 5.5 0.9 ± 9.2 0.1 ± 8.8 -1.5 ± 12 -0.7 ± 12
Zmax pos 0.1 ± 21 -4.6 ± 22 -2.3 ± 23 -2.2 ± 27 -1.0 ± 31 -1.8 ± 29
Zmax amp 0.1 ± 3.1 -0.7 ± 4.3 -1.7 ± 14 -0.7 ± 7.4 0.7 ± 12 0.1 ± 12
Nadir pos -0.6 ± 10.2 -2.5 ± 17 -0.5 ± 12 -0.4 ± 12 -1.0 ± 17 1.9 ± 27
Classification 98% 100% 96% 96% 98% 98%
Median error, in percentage, and corresponding 95% limits of agreement (derived from the Bland-Altman analysis) of the measured P wave
morphology parameters, as a function of the recording length. The row Classification gives the percentage correctly classified. All comparisons
were made using a standard 6-min recording. (LR = low signal resolution)

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In two previous studies carried out by our group it has
been shown that analysis of P wave morphology may
reveal differences that would not have been detectable
had standard methods been used [2,7]. It is theoretically
plausible that these changes are the result of different pref-
erential interatrial conduction routes in these subjects [7].
Preliminary, yet unpublished, data from invasive studies
support this hypothesis. The present study clearly shows
that, although not based on formalised criteria, the man-
ual classification of P wave morphology is highly repro-
ducible, with low intra- and interrater variability. The
results of the classification algorithm are naturally totally
reproducible as they are based on strict criteria. The mar-
ginally poorer agreement between the automated and the
final manual classification, compared with the intra- and
interrater agreement in manual classification, may be the
price one has to pay to ensure reproducibility.
Recording length
In the present study there were no signs of declining per-
formance as a result of shortening the recording time,
even when considering only the recordings with the
fewest averaged P waves (i.e. three to five). The method
was also robust when recordings with lower sampling res-
olution were analysed. This has potentially important
implications since many commercially available ECG
storing systems use this sampling resolution. The demon-
strated robustness, together with the sufficient recording
length of ten seconds, allows the use of ECGs stored using
such systems, at least if the ECGs are of sufficiently 'high'
quality.
Study limitations
All the ECG recordings used in the present study were of
high quality. Therefore, no inferences regarding the per-
formance of the method on ECGs of poor quality can be
made.
Conclusion
The present study illustrates a marked difference in P wave
duration depending on the methodology employed, thus
explaining differences in absolute values reported by dif-
ferent groups. It is therefore necessary to define each
method carefully or to recommend a universal method of
estimating P wave duration.
The automated analysis of P wave duration and morphol-
ogy demonstrated in this study has high reproducibility
and is not affected by the length of the recording. As short
as ten-second-long ECGs recorded and stored using com-
mercially available systems with lower signal resolution
can be used for non-invasive studies of interatrial conduc-
tion. The proposed automated classification algorithm
also showed excellent agreement with manually defined P
wave morphologies.
Competing interests
The author(s) declare that they have no competing inter-
ests.
Bandpassed-filtered signal-averaged P wave analysisFigure 5
Bandpassed-filtered signal-averaged P wave analysis.
A representative example of a bandpass-filtered (40–250 Hz),
signal-averaged P wave in which there is an evident depend-
ency between the noise threshold used and the estimate of
the P wave duration.
0 50 100 150 200 250 300
0.05
0.10
0.20
[ms]
[µV]
Median error in P wave duration as a result of recording lengthFigure 4
Median error in P wave duration as a result of
recording length. Median error (%) in the P wave duration
estimate as a result of recording length. L1 = 6 min; L2 = 3
min; L3 = 1 min; L4 = 30 s; L5 = 10 s, and L6 = 10 s recording
length with lower sampling resolution (see text).

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Authors' contributions
FH designed the study, included patients, analysed and
interpreted the data and drafted the manuscript. PGP
interpreted data and revised the manuscript. RH included
patients, analysed and interpreted data and revised the
manuscript. JC co-designed the study, analysed and inter-
preted data and revised the manuscript. All authors read
and approved the final manuscript.
Acknowledgements
This study was supported by grants from Lund University.
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Pre-publication history
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