Combination of Hedge Algebra and Type-2 Fuzzy System for Electrocardiogram Signal Recognition and Classification
Main Article Content
Abstract
The paper presents a new application of Hedge Algebra in Type 2 (HAT2) Fuzzy System (FS) for electrocardiographical signal recognition and classification. The HAT2 integrates the capability of fuzzy logic in modeling uncertainties of data and the linguistic variables to describe and to manipulate the human knowledge. The proposed approach will be tested with different types of arrhythmia in the ECG signals taken from the MIT - BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Databases. The numerical results will be compared with other methods to show the high quality of proposed solution. The proposed solution is also simple with low complexity of computation, which makes it suitable for use in IoT or portable systems.
Keywords
Type-2 fuzzy logic, hedge algebra, arrhythmia recognition
Article Details
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References
[1] Liu W., Huang Q., Chang Sh., Wang H., He J.,
Multiple-feature-branch convolutional neural
network for myocardial infarction diagnosis using
electrocardiogram, Biomedical Signal Processing
and Control, vol. 45, pp. 22-32, 2018.
https://doi.org/10.1016/j.bspc.2018.05.013
[2] Śmigiel S., Pałczyński K. and Ledziński D., ECG
signal classification using deep learning techniques
based on the PTB-XL dataset, Entropy (Basel). 2021
Aug 28;23(9):1121.
https://doi.org/10.3390/e23091121
[3] Andreotti F., Carr O., Pimentel M., Mahdi A., De
Vos M., Comparing feature-based classifiers and
convolutional neural networks to detect arrhythmia
from short segments of ECG, IEEE Computing in
Cardiology (CINC), pp. 1-4, 2017.
https://doi.org/10.22489/CinC.2017.360-239
[4] Sayantan G., Kien P.T., Kadambari K.V.,
Classification of ECG beats using deep belief
network and active learning, Medical & Biological
Engineering & Computing, 56 (10) pp. 1887-1898,
2018.
https://doi.org/10.1007/s11517-018-1815-2
[5] Wu M., Lu Y., Yang W. and Wong S.Y., A study on
arrhythmia via ECG signal classification using the
convolutional neural network, Front. Comput.
Neurosci. 14:564015, 2021.
https://doi.org/10.3389/fncom.2020.564015
[6] Osowski S., Linh T.H., Markiewicz T., Support
vector machine based expert system for reliable heart
beat recognition, IEEE Transactions on Biomedical
Engineering, vol. 51, no. 4, pp. 582-589, 2004.
https://doi.org/10.1109/TBME.2004.824138
[7] Odinaka I., Po-Hsiang Lai, Kaplan A. D., O'Sullivan
J. A., Sirevaag E. J., Rohrbaugh J. W., ECG
biometric recognition: a comparative analysis, IEEE
Transactions on Information Forensics and Security,
vol. 7, no. 6, pp. 1812-1824, 2012.
https://doi.org/10.1109/TIFS.2012.2215324
[8] Zahra Ebrahimi, Mohammad Loni, Masoud
Daneshtalab and Arash Gharehbaghi, A review on
deep learning methods for ECG arrhythmia
classification, Expert Systems with Applications: X,
Volume 7, 100033, 2020.
https://doi.org/10.1016/j.eswax.2020.100033
[9] Castillo O. and Melin P., Type-2 Fuzzy Logic -
Theory and Applications, Springer-Verlag, Berlin
Heidelberg, 2008.
https://doi.org/10.1007/978-3-540-76284-3
[10] Zadeh L.A., The conception of a linguistic variable
and its application in approximate reasoning - I,
Information Science, vol. 8, pp. 199-249, 1975.
https://doi.org/10.1016/0020-0255(75)90036-5
[11] Ho N. C. and Wechler W., Extended hedge algebras
and their application to fuzzy logic, Fuzzy Sets and
Systems, vol. 52, pp. 259-281, 1992.
https://doi.org/10.1016/0165-0114(92)90237-X
[12] Linh T.H., Nam P.V. and Nam V.H., Multiple neural
networks integration using binary decision tree to
improve the ecg signal recognition accuracy,
Applied Mathematics and Computer Science, vol.
24, no. 3, pp. 647-655, 2014.
https://doi.org/10.2478/amcs-2014-0047
[13] Khang T. D., Phong P. A., Dong D. K. and Trang
C.M., Hedge algebraic Type 2 fuzzy sets, in Proc.
FUZZ-IEEE 2010, in conjunction with the WCCI
2010, Spain, pp. 1850-1857, 2010.
https://doi.org/10.1109/FUZZY.2010.5584108
[14] Haykin S., Neural Networks: A Comprehensive
Foundation, 2nd ed., Prentice-Hall, Englewood
Cliffs, NJ, 1999.
Multiple-feature-branch convolutional neural
network for myocardial infarction diagnosis using
electrocardiogram, Biomedical Signal Processing
and Control, vol. 45, pp. 22-32, 2018.
https://doi.org/10.1016/j.bspc.2018.05.013
[2] Śmigiel S., Pałczyński K. and Ledziński D., ECG
signal classification using deep learning techniques
based on the PTB-XL dataset, Entropy (Basel). 2021
Aug 28;23(9):1121.
https://doi.org/10.3390/e23091121
[3] Andreotti F., Carr O., Pimentel M., Mahdi A., De
Vos M., Comparing feature-based classifiers and
convolutional neural networks to detect arrhythmia
from short segments of ECG, IEEE Computing in
Cardiology (CINC), pp. 1-4, 2017.
https://doi.org/10.22489/CinC.2017.360-239
[4] Sayantan G., Kien P.T., Kadambari K.V.,
Classification of ECG beats using deep belief
network and active learning, Medical & Biological
Engineering & Computing, 56 (10) pp. 1887-1898,
2018.
https://doi.org/10.1007/s11517-018-1815-2
[5] Wu M., Lu Y., Yang W. and Wong S.Y., A study on
arrhythmia via ECG signal classification using the
convolutional neural network, Front. Comput.
Neurosci. 14:564015, 2021.
https://doi.org/10.3389/fncom.2020.564015
[6] Osowski S., Linh T.H., Markiewicz T., Support
vector machine based expert system for reliable heart
beat recognition, IEEE Transactions on Biomedical
Engineering, vol. 51, no. 4, pp. 582-589, 2004.
https://doi.org/10.1109/TBME.2004.824138
[7] Odinaka I., Po-Hsiang Lai, Kaplan A. D., O'Sullivan
J. A., Sirevaag E. J., Rohrbaugh J. W., ECG
biometric recognition: a comparative analysis, IEEE
Transactions on Information Forensics and Security,
vol. 7, no. 6, pp. 1812-1824, 2012.
https://doi.org/10.1109/TIFS.2012.2215324
[8] Zahra Ebrahimi, Mohammad Loni, Masoud
Daneshtalab and Arash Gharehbaghi, A review on
deep learning methods for ECG arrhythmia
classification, Expert Systems with Applications: X,
Volume 7, 100033, 2020.
https://doi.org/10.1016/j.eswax.2020.100033
[9] Castillo O. and Melin P., Type-2 Fuzzy Logic -
Theory and Applications, Springer-Verlag, Berlin
Heidelberg, 2008.
https://doi.org/10.1007/978-3-540-76284-3
[10] Zadeh L.A., The conception of a linguistic variable
and its application in approximate reasoning - I,
Information Science, vol. 8, pp. 199-249, 1975.
https://doi.org/10.1016/0020-0255(75)90036-5
[11] Ho N. C. and Wechler W., Extended hedge algebras
and their application to fuzzy logic, Fuzzy Sets and
Systems, vol. 52, pp. 259-281, 1992.
https://doi.org/10.1016/0165-0114(92)90237-X
[12] Linh T.H., Nam P.V. and Nam V.H., Multiple neural
networks integration using binary decision tree to
improve the ecg signal recognition accuracy,
Applied Mathematics and Computer Science, vol.
24, no. 3, pp. 647-655, 2014.
https://doi.org/10.2478/amcs-2014-0047
[13] Khang T. D., Phong P. A., Dong D. K. and Trang
C.M., Hedge algebraic Type 2 fuzzy sets, in Proc.
FUZZ-IEEE 2010, in conjunction with the WCCI
2010, Spain, pp. 1850-1857, 2010.
https://doi.org/10.1109/FUZZY.2010.5584108
[14] Haykin S., Neural Networks: A Comprehensive
Foundation, 2nd ed., Prentice-Hall, Englewood
Cliffs, NJ, 1999.