International audienceA precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed

Marinho R. de Oliveira, Pedro

Rolim Fernandes, C. Alexandre

Zarzoso, Vicente

English

Archive Ouverte en Sciences de l'Information et de la Communication

HAL Id: hal-02301040
https://hal.archives-ouvertes.fr/hal-02301040
Submitted on 30 Sep 2019
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
Source Classification in Atrial Fibrillation Using a
Machine Learning Approach
Pedro Marinho R. de Oliveira, Vicente Zarzoso, C. Alexandre Rolim Fernandes
To cite this version:
Pedro Marinho R. de Oliveira, Vicente Zarzoso, C. Alexandre Rolim Fernandes. Source Classification
in Atrial Fibrillation Using a Machine Learning Approach. Computing in Cardiology 2019 (CINC
2019), Sep 2019, Singapour, Singapore. ￿hal-02301040￿
Source Classification in Atrial Fibrillation Using a Machine Learning Approach
Pedro Marinho R. de Oliveira1, Vicente Zarzoso1, Carlos Alexandre R. Fernandes2
1 Universite´ Coˆte d’Azur, CNRS, I3S Laboratory, Sophia Antipolis, France
2 Universidade Federal do Ceara´, Sobral, Brazil
Abstract
A precise analysis of the atrial activity (AA) signal in
electrocardiogram (ECG) recordings is necessary for a
better understanding of the mechanisms behind atrial fib-
rillation (AF). Blind source separation (BSS) techniques
have proven useful in extracting the AA source from ECG
recordings. However, the automated selection of the AA
source among the other sources after BSS is still an issue.
In this scenario, the present work proposes two contribu-
tions: i) the use of the normalized mean square error of
the TQ segment (NMSE-TQ) as a new feature to quantify
the AA content of a source, and ii) an automated classifi-
cation of AA and non-AA sources using three well-known
machine learning algorithms. The tested classifiers out-
perform the techniques present in literature. A pattern in
the mean and standard deviation of the used features, for
AA and non-AA sources, is also observed.
1. Introduction
Decreasing life quality and increasing healthcare costs,
atrial fibrillation (AF) is a supraventricular tachyarrhyth-
mia characterized by an uncoordinated and irregular atrial
activation [1]. Persistent AF represents a particularly com-
plex case of this arrhythmia, where extensive atrial remod-
eling has taken place due to sustained AF, significantly af-
fecting atrial activity (AA) and AF perpetuation itself. This
challenging cardiac condition represents a major health,
social and economical concern. Also, the mechanisms be-
hind AF are complex and not completely understood, in-
creasing the intensity of clinical research into this cardiac
rhythm disturbance in the past few years.
Signal processing techniques are important and neces-
sary tools to noninvasively separate the AA from the stan-
dard 12-lead electrocardiogram (ECG), for a precise anal-
ysis and characterization of the f waves, in order to bet-
ter understand the complex mechanisms behind AF. The
extraction of AA from multi-lead ECGs accepts a blind
source separation (BSS) formulation [2] and methods to
solve BSS problems, such as principal component analy-
sis (PCA) [3], independent component analysis (ICA) [4]
and block term decomposition (BTD) [5] are reported in
the literature as useful tools for noninvasive AA extrac-
tion [2], [6–10].
However, after separating the sources that compose the
ECG recording, one needs to select the atrial source es-
timate among the other sources. Even with the help of
some parameters for AA content measurement, there is
still no optimal automated method for atrial source selec-
tion, which often needs to be done by visual inspection to
achieve optimality.
To provide more information about the AA content of an
estimated source and improve the automated atrial source
selection accuracy, the present work proposes the normal-
ized mean square error of the TQ segment (NMSE-TQ)
between the original recording and the estimated source as
a new criterion to quantify AA content. Furthermore, an
automated approach for classification of AA and non-AA
sources is put forward, using three different machine learn-
ing algorithms: linear and quadratic discriminant anal-
ysis (LDA and QDA) [11], and support vector machine
(SVM) [12], showing that this approach provides better ac-
curacy than the techniques reported in the literature.
2. Methods
2.1. Blind Source Separation Techniques
Signal processing techniques that solve BSS problems
separate the observed ECG signal matrix Y in a linear com-
bination of a mixing matrix M and a source matrix S:
Y = MS ∈ RK×N . (1)
In the present case of study, Y ∈ RK×N is the AF ECG
data matrix, composed of K signals (leads) and N sam-
ples, M ∈ RK×R is the mixing matrix, modeling the prop-
agation of the R cardiac electrical sources from the heart
to the K leads in the body surface, and S ∈ RR×N is the
source matrix that contains R sources, mainly atrial, ven-
tricular and noise sources.
In this work, three different BSS methods are applied in
the observed recordings in order to compute an estimate
of the sources that compose the original AF ECG, as de-
scribed below.
1) PCA: This matrix-based method performs an orthog-
onal linear transformation in the original data, resulting in
principal components that are mutually uncorrelated [3].
The orthogonal linear transformation is given by the diag-
onalization of Rˆy = 1N YY
T .
When applied in multi-lead AF ECG recordings, PCA
exploits inter-lead correlation, decomposing the recording
so that the first principal components represent the ven-
tricular activity (VA) source, whereas the next ones corre-
spond to the AA source, noises, muscular activity, etc.
2) RobustICA-f: ICA decomposes the observed data
into statistically independent sources. RobustICA-f is a
variant of ICA that performs exact line search optimization
of the kurtosis contrast function in the frequency domain
[13]. This method does not require sources to be spatially
orthogonal and, since AA and VA during AF episodes are
assumed to be statistically independent, such technique is
more suitable for AA extraction compared to PCA.
3) Hankel-based BTD: This tensor factorization tech-
nique is based on a third-order tensor Y built from Han-
kel matrices that are constructed from each row of the ob-
served data matrix. The tensor is then decomposed as:
Y =
R∑
r=1
Er ◦ c.r (2)
where ◦ represents the outer product, c.r is a nonzero vec-
tor, Er is a Hankel matrix built from each source and R
is the number of sources. This technique suits the charac-
teristics of AA during AF, since atrial signals can be ap-
proximated by all-pole models and mapped onto Hankel
matrices with rank equal to the number of poles [7].
2.2. Classification Algorithms
Classifiers are machine learning algorithms that operate
on labeled data, predicting in which class (or category) the
data belong to. Such algorithms are also defined as su-
pervised learning models, as they need labeled data (train-
ing set) to learn and perform the classification of new data
points (testing set). Three well-known classifiers are used
in this work to classify the source estimates provided by
the BSS techniques, introduced in the previous section,
into AA and non-AA sources.
1) LDA: Commonly used for data classification and di-
mensionality reduction, LDA guarantees maximal sepa-
rability with a linear decision region between the classes
by computing discriminant scores for each observation ob-
tained by a linear combination of the training data. LDA
assumes that the data are normally distributed and all
classes identically distributed.
2) QDA: Unlike LDA, this method separates the given
classes with a quadratic decision region. In this method,
the discriminant scores for each observation are obtained
by a non-linear combination of the training data. Since
QDA has more degrees of freedom, it tends to perform bet-
ter than LDA. However, if the database is too small, LDA
may provide a better performance.
3) SVM: This popular classifier aims to separate the
classes by creating a linear (or non-linear) decision hyper-
plane. An optimal separating hyperplane can be found by
minimizing the distance between misclassified data points
and the decision margin. These misclassified observations
are called support vectors, as they determine how SVM
discriminates between classes, supporting the classifica-
tion. In the present work, a Gaussian kernel is used.
Three parameters are extracted and used as features for
classification: spectral concentration (SC) in % [6], kurto-
sis of the signal in frequency domain [8] and the proposed
NMSE-TQ, given by:
NMSE-TQ =
[
||m(V 1)r sr. − y(V 1)||2F
||y(V 1)||2F
]
TQ
(3)
where || · ||F stands for the Frobenius norm, m(V 1)r is the
contribution of the rth source estimate to lead V1, sr. is
the rth source estimate and y(V 1) is the original recording
on lead V1. Note that lead V1 is chosen as reference for
this parameter, since this lead is the one that typically best
reflects AA in AF ECGs.
3. Experimental Results
3.1. Database and Experimental Setup
All ECG recordings belong to a database provided
by the Cardiology Department of Princess Grace Hospi-
tal Center, Monaco. The recordings are acquired at a
977 Hz sampling rate and are preprocessed by a zero-
phase forward-backward type-II Chebyshev bandpass filter
with cutoff frequencies of 0.5 and 40 Hz, in order to sup-
press high-frequency noise and baseline wandering. Ex-
periments are performed on different segments of ECG
recordings from 30 different patients suffering from per-
sistent AF, processing all the 12 leads.
The Hankel-based BTD is implemented using the non-
linear least squares (NLS) method available in Tensorlab
MATLAB toolbox [14] choosing R = 6 and Lr = L, for
r = 1, 2, ..., R, withL taking values in the set {17, 48, 95}.
The choice of R is based on the singular value decompo-
sition (SVD) of the observed data matrix, taking into ac-
count the most significant singular values. The choice of
Lr is based on the work [7], which showed that such val-
ues provided satisfactory results for the heartbeat with the
largest TQ segment of one of the patients in the considered
database. Also, in [9] it was showed that Lr = 48 provides
satisfactory results for consecutive segments of the whole
ECG recording of the same patient.
The randomly chosen recordings are from 0.82 to 1.75
seconds in length and are downsampled by a factor of two,
since the 3rd-order tensors built from the original sample
rate pose some difficulties to Tensorlab. For the matrix-
based techniques PCA and RobustICA-f, no downsam-
pling is needed. Monte Carlo runs with Gaussian random
initialization for the spacial and temporal factors of BTD
at each run are used. Monte Carlo runs are needed since
the performance of BTD depends strongly on the initial-
ization of its factors and a suitable initialization is still an
open challenge. These three BSS methods are applied to
short ECG recordings from our AF patient database, gen-
erating 1283 sources that are visually labeled as 551 AA
sources and 732 non-AA sources.
3.2. Classification
After performing BSS using the techniques mentioned
in Section 2.1, the three features mentioned in the previ-
ous section are extracted from the estimated sources. Us-
ing all of these three features, LDA, QDA and SVM are
applied to classify the database in AA sources and non-
AA sources. Figure 1 illustrates these two classes for one
of the patients, as it can be seen the original recording
in gray and some sources estimates by the Hankel-based
BTD. The AA source is shown in blue, followed by non-
AA sources. In this case the VA source estimate and noises
of different amplitudes can be seen.
From the 1283 generated source estimates, 1046 are
used for training the classifiers, while 237 are used for test-
ing. A cross-validation with 5 partitions, i.e., 5-fold cross-
validation, is performed in all classifiers. Figure 2 illus-
trates the accuracy performance of all the methods com-
pared in this work. The parameter SC by itself provides
an accuracy of 49.90% (the signal with the highest SC
is chosen as AA source), being outperformed by the two
techniques PM1 and PM2 proposed in [8], which provide
65.66% and 74.53%, respectively. The proposed param-
eter NMSE-TQ on its own provides 73.07% of accuracy
(the signal with the lowest NMSE-TQ is chosen as AA
source), almost the same performance as PM2, while the
LDA, QDA and SVM classifiers provide 84.39%, 87.34%
and 91.98% of accuracy, respectively. As expected, QDA
performs better than LDA, as it uses a non-linear region
to separate the classes, which seems to suit better the AA
source classification problem in AF.
It is valid to state that if the AA is represented by more
than one source, in a particular ECG recording, the classi-
fiers are able to discriminate all of them from the non-AA
sources. This is not possible with the existing techniques
0 0.2 0.4 0.6 0.8 1 1.2 1.4
Time (s)
Original Recording
AA Source Estimate
VA Source Estimate
Strong Noise
Weak Noise
Figure 1. Original ECG recording, AA source estimate
and non-AA source estimates (VA and noises) on lead V1.
The signals are vertically shifted for clarity.
Table 1. Confusion matrix of LDA, QDA and SVM. AcA:
actual AA sources, AcN: actual non-AA sources, PrA: pre-
dicted AA sources and PrN: predicted non-AA sources.
N = 237 LDA QDA SVMPrA PrN PrA PrN PrA PrN
AcA 82 26 108 0 99 9
AcN 11 118 30 99 10 119
here compared, since they are based on the assumption that
the AA is represented by a single source.
Table 1 shows the confusion matrix of the three clas-
sifiers. It can be seen that LDA misclassifies the largest
number of sources, with 37 misclassified sources (26 AA
and 11 non-AA sources). QDA correctly classifies all the
AA sources, but misclassifies more non-AA sources than
LDA (30 non-AA sources). SVM, the classifier that pro-
vides the highest accuracy, misclassifies the shortest num-
ber of sources, with 19 misclassified sources (9 AA and 10
non-AA sources).
It was also observed that the mean and standard devi-
ation (µ ± σ) of SC, kurtosis and NMSE-TQ for the AA
sources was 61.8±15.1, 141.3±65.4 and 1.6±3.0, while
for the non-AA sources was 37.8± 14.5, 41.7± 29.1 and
34.9± 111.0, respectively. It can be seen that AA sources
have very small NMSE-TQ values concentrated in a short
range, while non-AA sources have big NMSE-TQ values
varying in a long range. This shows that the proposed pa-
rameter discriminates very well these signals, which ex-
plains why it provides a satisfactory performance when
used alone for classification in this challenging applica-
010
20
30
40
50
60
70
80
90
100
Ac
cu
ra
cy
 (%
)
SC [6]
PM1 [8]
PM2 [8]
NMSE-TQ
LDA
QDA
SVM
Figure 2. AA source selection accuracy by different tech-
niques: SC proposed in [6]; PM1 and PM2 proposed in
[8]; NMSE-TQ proposed in the present work; and the three
classifiers: LDA, QDA and SVM.
tion.
4. Conclusions
In order to better quantify the AA content of a source
estimate and improve the automated atrial source selection
accuracy, the present work has proposed a new criterion
called NMSE-TQ and the use of machine learning clas-
sifiers to discriminate the AA signals from the other es-
timates. The NMSE-TQ was shown to be a satisfactory
measure of AA content and also a relevant feature for clas-
sification. The three classifiers used in the present work
provided better accuracy than the existing techniques here
compared.
Future works will aim to assess the performance of other
classifiers as well as carry out an unsupervised classifica-
tion, i.e., classify the data without labels. Increasing the
database of AF patients in order to provide more relevant
clinical results is another a perspective.
Acknowledgments
Pedro Marinho R. de Oliveira is funded by a PhD schol-
arship from the IT Doctoral School (EDSTIC) of the Uni-
versite´ Coˆte d’Azur.
References
[1] C. T. January, L. S. Wann, J. S. Alpert, H. Calkins, J. C. Cleveland,
J. E. Cigarroa, J. B. Conti, et al., “2014 AHA/ACC/HRS guideline
for the management of patients with atrial fibrillation: A report of
the American College of Cardiology/American Heart Association
Task Force on practice guidelines and the Heart Rhythm Society”,
Circulation, vol. 64, no. 21, pp. 2246–2280, Dec. 2014.
[2] J. J. Rieta, F. Castells, C. Sa´nchez, V. Zarzoso, and J. Millet, “Atrial
activity extraction for atrial fibrillation analysis using blind source
separation”, IEEE Transactions on Biomedical Engineering, vol.
51, no. 7, pp. 1176–1186, Jul. 2004.
[3] I.T. Jolliffe, Principal Component Analysis, Wiley Online Library,
2005.
[4] P. Comon, “Independent component analysis, A new concept?”,
Signal Processing, no. 36, pp. 287–314, 1994.
[5] L. De Lathauwer, “Blind separation of exponential polynomials
and the decomposition of a tensor in rank-(lr , lr , 1) terms”, SIAM
Journal on Matrix Analysis and Applications, vol. 32, no. 4, pp.
1451–1474, 2011.
[6] F. Castells, J. J. Rieta, J. Millet, and V. Zarzoso, “Spatiotempo-
ral blind source separation approach to atrial activity estimation in
atrial tachyarrhythmias”, IEEE Transactions on Biomedical Engi-
neering, vol. 52, no. 2, pp. 258–267, Feb. 2005.
[7] V. Zarzoso, “Parameter estimation in block term decomposition
for noninvasive atrial fibrillation analysis”, in Proc. CAMSAP-
2017, IEEE International Workshop on Computational Advances
in Multi-Sensor Adaptive Processing, Curac¸ao, Dutch Antilles, pp.
1–5, Dec. 10-13, 2017.
[8] P. M. R. de Oliveira and V. Zarzoso, “Source analysis and selec-
tion using block term decomposition in atrial fibrillation”, in Proc.
LVA/ICA-2018, 14th International Conference on Latent Variable
Analysis and Signal Separation, Guildford, U.K., pp. 46–56, Jul.
2-6, 2018.
[9] P. M. R. de Oliveira and V. Zarzoso, “Temporal stability of block
term decomposition in noninvasive atrial fibrillation analysis”, in
Proc. Asilomar-2018, 52nd Annual Asilomar Conference on Sig-
nals, Systems, and Computers, Pacific Grove, U.S.A, pp. 816–820,
Oct. 28-31, 2018.
[10] P. M. R. de Oliveira and V. Zarzoso, “Block Term Decomposition
of ECG Recordings for Atrial Fibrillation Analysis: Temporal and
Inter-Patient Variability”, Journal of Communication and Informa-
tion Systems, vol. 34, no. 1, pp. 111-119, Apr. 2019
[11] R. A. Fisher, “The use of multiple measurements in taxonomic
problems”, Annals of Eugenics, vol. 7, pp. 179–188, 1936.
[12] B. E. Boser, I. M. Guyon, V. N. Vapnik, “A training algorithm for
optimal margin classifiers”, in Proc. COLT-1992, 5th Annual Work-
shop on Computational Learning Theory, Pittsburgh, U.S.A, pp.
144–152, Ju. 27–29, 1992.
[13] V. Zarzoso and P. Comon, “Robust independent component analy-
sis by iterative maximization of the kurtosis contrast with algebraic
optimal step size”, IEEE Transactions on Neural Networks, vol. 21,
no. 2, pp. 248–261, 2010.
[14] N. Vervliet, O. Debals, L. Sorber, M. Van Barel, and L. De
Lathauwer, Tensorlab 3.0, Available online, Mar. 2016. URL:
https://www.tensorlab.net/
Address for correspondence:
Pedro Marinho R. de Oliveira
Universite´ Coˆte d’Azur, CNRS, I3S Laboratory
06903 Sophia Antipolis Cedex
marinho@i3s.unice.fr


Source Classification in Atrial Fibrillation Using a Machine Learning Approach

https://hal.archives-ouvertes.fr/hal-02301040/document

Source Classification in Atrial Fibrillation Using a Machine Learning Approach

Abstract

Similar works

Full text

Available Versions

Archive Ouverte en Sciences de l'Information et de la Communication