AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home International Journal of Crowd Science Article
PDF (1.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

ECG Signal Processing and Automatic Classification Algorithms

Xiaonuo Yang1, Yueting Chai1( )
National Engineering Laboratory for E-Commerce Technologies, Department of Automation, Tsinghua University, Beijing 100084, China
Show Author Information

Abstract

With heart health issues attracting much attention, wearable electrocardiogram (ECG) monitoring devices show a broad market prospect. This paper develops a generic ECG pre-processing algorithm and proposes a method for the single-lead ECG classification problem based on model stacking. Features such as RR-intervals, power spectrum, and higher-order statistics are computed and grouped into three classes. The support vector machine (SVM) classifier is trained separately based on each class of features, and subsequently, a fourth SVM classifier is trained on the prediction results of the three SVM classifiers at the first layer. To obtain more realistic results and better comparisons with previous studies, the algorithm follows the ANSI/AAMI EC57:2012 standard and is tested on a real ECG database. The experimental results show that the algorithm in this paper better overcomes the impact of the data imbalance problem and obtains good results.

Keywords

electrocardiogram (ECG)ECG classificationmodel stacking

References

[1]

S. L. Grace, K. I. Turk-Adawi, A. Contractor, A. Atrey, N. Campbell, W. Derman, G. L. M. Ghisi, N. Oldridge, B. K. Sarkar, T. J. Yeo, et al., Cardiac rehabilitation delivery model for low-resource settings, Heart Br. Card. Soc., vol. 102, no. 18, pp. 1449–1455, 2016.
Crossref Google Scholar
[2]

N. J. Holter, New method for heart studies: Continuous electrocardiography of active subjects over long periods is now practical, Science, vol. 134, no. 3486, pp. 1214–1220, 1961.
Crossref Google Scholar
[3]

C. Gopalsamy, S. Park, R. Rajamanickam, and S. Jayaraman, The wearable motherboard™: The first generation of adaptive and responsive textile structures (ARTS) for medical applications, Virtual Real., vol. 4, no. 3, pp. 152–168, 1999.
Crossref Google Scholar
[4]
P. D. Chazal, Detection of supraventricular and ventricular ectopic beats using a single lead ECG, in Proc. 2013 35th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013, pp. 45–48.
Crossref
[5]

Y. Gu, G. Zheng, and M. Dai, A morphology algorithm based on 2-dimensional flat structure element on ECG baseline wander elimination, Computing in Cardiology, vol. 38, pp. 817–820, 2011.
Google Scholar
[6]

A. Singhal, P. Singh, B. Fatimah, and R. B. Pachori, An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique, Biomed. Signal Process. Contr., vol. 57, p. 101741, 2020.
Crossref Google Scholar
[7]
W. Hao, Y. Chen, and Y. Xin, ECG baseline wander correction by mean-median filter and discrete wavelet transform, in Proc. 2011 Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 2011, pp. 2712–2715.
[8]
R. Verma, R. Mehrotra, and V. Bhateja, An integration of improved median and morphological filtering techniques for electrocardiogram signal processing, in Proc. 2013 3rd IEEE Int. Advance Computing Conference (IACC), Ghaziabad, India, 2013, pp. 1223–1228.
Crossref
[9]
S. S. Bhogeshwar, M. K. Soni, and D. Bansal, Design of simulink model to denoise ECG signal using various IIR & FIR filters, in Proc. 2014 Int. Conf. Reliability Optimization and Information Technology (ICROIT), Faridabad, India, 2014, pp. 477–483.
Crossref
[10]

H. D. Hesar and M. Mohebbi, An adaptive Kalman filter bank for ECG denoising, IEEE J. Biomed. Health Inform., vol. 25, no. 1, pp. 13–21, 2021.
Crossref Google Scholar
[11]
M. Rakshit and S. Das, An improved EMD based ECG denoising method using adaptive switching mean filter, in Proc. 2017 4th Int. Conf. Signal Processing and Integrated Networks (SPIN), Noida, India, 2017, pp. 251–255.
Crossref
[12]

S. Suppappola and Y. Sun, Nonlinear transforms of ECG signals for digital QRS detection: A quantitative analysis, IEEE Trans. Biomed. Eng., vol. 41, no. 4, pp. 397–400, 1994.
Crossref Google Scholar
[13]

D. Benitez, P. A. Gaydecki, A. Zaidi, and A. P. Fitzpatrick, The use of the Hilbert transform in ECG signal analysis, Comput. Biol. Med., vol. 31, no. 5, pp. 399–406, 2001.
Crossref Google Scholar
[14]
I. Saini, D. Singh, and A. Khosla, Delineation of ECG wave components using K-nearest neighbor (KNN) algorithm: ECG wave delineation using KNN, in Proc. 2013 10th Int. Conf. Information Technology: New Generations, Las Vegas, NV, USA, 2013, pp. 712–717.
Crossref
[15]
M. Sarlija, F. Jurisic, and S. Popovic, A convolutional neural network based approach to QRS detection, in Proc. 10th Int. Symp. on Image and Signal Processing and Analysis, Ljubljana, Slovenia, 2017, pp. 121–125.
Crossref
[16]
G. D. Lannoy, D. François, J. Delbeke, and M. Verleysen, Weighted SVMs and feature relevance assessment in supervised heart beat classification, in Proc. 3rd Int. Joint Conf. Biomedical Engineering Systems and Technologies, Valencia, Spain, 2011, pp. 212–223.
Crossref
[17]

V. Mondéjar-Guerra, J. Novo, J. Rouco, M. G. Penedo, and M. Ortega, Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers, Biomed. Signal Process. Contr., vol. 47, pp. 41–48, 2019.
Crossref Google Scholar
[18]

S. Padmavathi and E. Ramanujam, Naïve Bayes classifier for ECG abnormalities using multivariate maximal time series motif, Procedia Comput. Sci., vol. 47, pp. 222–228, 2015.
Crossref Google Scholar
[19]

G. Petmezas, K. Haris, L. Stefanopoulos, V. Kilintzis, A. Tzavelis, J. A. Rogers, A. K. Katsaggelos, and N. Maglaveras, Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets, Biomed. Signal Process. Contr., vol. 63, p. 102194, 2021.
Crossref Google Scholar
[20]
F. Andreotti, O. Carr, M. A. F. Pimentel, A. Mahdi, and M. D. Vos, Comparing feature based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG, in Proc. 2017 Computing in Cardiology Conference (CinC), Rennes, France, 2017, pp. 1–4.
Crossref
[21]

L. Guo, G. Sim, and B. Matuszewski, Inter-patient ECG classification with convolutional and recurrent neural networks, Biocybern. Biomed. Eng., vol. 39, no. 3, pp. 868–879, 2019.
Crossref Google Scholar
[22]

P. D. Chazal and R. B. Reilly, A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2535–2543, 2006.
Crossref Google Scholar
[23]

J. S. Richman and J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. Heart Circ. Physiol., vol. 278, no. 6, pp. H2039–H2049, 2000.
Crossref Google Scholar
[24]

M. Kumar, R. Pachori, and U. Acharya, Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework, Entropy, vol. 19, no. 9, p. 488, 2017.
Crossref Google Scholar
[25]

R. Alcaraz and J. J. Rieta, A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms, Biomed. Signal Process. Contr., vol. 5, no. 1, pp. 1–14, 2010.
Crossref Google Scholar
[26]

P. D. Chazal, M. O’Dwyer, and R. B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1196–1206, 2004.
Crossref Google Scholar
[27]

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, SMOTE: Synthetic minority over-sampling technique, J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002.
Crossref Google Scholar
International Journal of Crowd Science
Volume 8 Issue 3,
September 2024
Pages 122-129
DOI: 10.26599/IJCS.2023.9100026
Cite this article:
Yang X, Chai Y. ECG Signal Processing and Automatic Classification Algorithms. International Journal of Crowd Science, 2024, 8(3): 122-129. https://doi.org/10.26599/IJCS.2023.9100026

62

Views

1

Downloads

0

Crossref

0

Scopus

Altmetrics
Received: 10 February 2023
Revised: 20 September 2023
Accepted: 21 September 2023
Published: 19 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return