Automated detection of anomalies in electrocardiograms using Empirical Mode Decomposition

Hygor Santiago, Milton Dias


Study objective: Develop an algorithm for detection and classification of heart arrhythmia in electrocardiograms.

Methodology/approach: Different ways of using the collected data are discussed, starting from the simplest one, which is the beat counter, to the more complex ones, where the complete signals present in an electrocardiogram is used. Different Machine Learning techniques were also used: K-Nearest Neighbors, Logistic Regression, Support Vector Machines and Extra Trees. The beat counter approach considers the time difference between each cardiac cycle and can be collected by a simple smart watch or an oximeter. For the complete classification of the anomalies, two other signal processing techniques were considered: the Fourier Transform and the Empirical Mode Decomposition.

Originality/Relevance: It is the first paper to use the Empirical Mode Decomposition combined with Machine Learning techniques for the classification and detection of cardiac anomalies.

Main results: The beat counter is not efficient enough to distinguish between all classes of existing anomalies, even among those studied in this work, but it presents good results for binary distinction between normal and abnormal heart beat. For the complete classification of the anomalies, the Empirical Mode Decomposition presented the best results. It is even better than the time-frequency analysis technique used in other papers on electrocardiogram classification.

Theoretical/methodological contributions: This paper presents a new application for Empirical Mode Decomposition and how it can be combined with classification techniques.


Electrocardiograph; Empirical Mode Decomposition; Machine Learning; Classification; Cardiac Anomaly Detector; Arrhythmia Detection.

Texto completo:

PDF (English)


Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H. & Adam, M. (2017). Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences, v. 405, p. 81–90,

Allen, C., Andrei, C. L., Carrero, J. J. & Goulart, A. C. (2017). Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the global burden of disease study 2016. The Lancet, v. 390, n. 10100, p. 1151–1210,

Chen, Y., Zheng, W., Li, W. & Huang, Y. (2021). Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognition Letters, v. 144, p. 1–5,10.1016/j.patrec.2021.01.008.

Das, M. & Ari, S. (2013). Analysis of ECG signal denoising method based on s-transform. IRBM, v. 34, n. 6, p. 362–370,10.1016/j.irbm.2013.07.012.

Escobar, F. B. (2019). Eletrocardiograma e anormalidades cardíacas. [S.l.: s.n.].

Fan, Y., Bai, J., Lei, X., Zhang, Y., Zhang, B., Li, K.-C. & Tan, G. (2020). Privacy preserving based logistic regression on big data. Journal of Network and Computer Applications, v. 171, p. 102769,

Jain, S., Ahirwal, M., Kumar, A., Bajaj, V. & Singh, G. (2017). QRS detection using adaptive filters: A comparative study. ISA Transactions, v. 66, p. 362–375,10.1016/j.isatra.2016.09.023.

Jiang, Y., Wang, X.-G., Zou, Z.-J. & Yang, Z.-L. (2021). Identification of coupled response models for ship steering and roll motion using support vector machines. Applied Ocean Research, v. 110, p. 102607,

Josephus, B. O., Nawir, A. H., Wijaya, E., Moniaga, J. V. & Ohyver, M. (2020). Predict mortality in patients infected with covid-19 virus based on observed characteristics of the patient using logistic regression. Procedia Computer Science, v. 179, p. 871–877, 2021, 5th International Conference on Computer Science and Computational Intelligence, 10.1016/j.procs.2021.01.076.

Li, Q., Rajagopalan, C. & Clifford, G. D. A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, v. 117, n. 3, p. 435–447, 2014.

Ma, H. & Li, J. (2021). A sub-linear time algorithm for approximating k-nearest-neighbor with full quality guarantee. Theoretical Computer Science, v. 857, p. 59–70, 10.1016/j.cmpb.2014.09.002.

Mailagaha Kumbure, M., Luukka, P. & Collan, M. (2020). A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recognition Letters, v. 140, p. 172–178, 10.1016/j.patrec.2020.10.005.

Makungwe, M., Chabala, L. M., Chishala, B. H. & Lark, R. M. (2021). Performance of linear mixed models and random forests for spatial prediction of soil ph. Geoderma, v. 397, p. 115079,


Martis, R. J., Acharya, U., Prasad, H., Chua, C. K. & Lim, C. M. Automated detection of atrial fibrillation using Bayesian paradigm. Knowledge-Based Systems, v. 54, p. 269–275.

Pławiak, P. (2018). Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Systems with Applications, v. 92, p. 334–349, 10.1016/j.eswa.2017.09.022.

Mohana, R. M., Reddy, C. K. K., Anisha, P. & Murthy, B. R. (2021). Random Forest algorithms for the classification of tree-based ensemble. Materials Today: Proceedings, 10.3390/ma14185342.

Nakagawa, S., Hochin, T., Nomiya, H., Nakanishi, H. & Shoji, M. (2021). Prediction of unusual plasma discharge by using support vector machine. Fusion Engineering and Design, v. 167, p. 112360.

Panhalkar, A. R. & Doye, D. D. (2021). Optimization of decision trees using modified African buffalo algorithm. Journal of King Saud University - Computer and Information Sciences, 10.1016/j.jksuci.2021.01.011.

Purcell, C. A., Alvis-Guzman, N., Bensenor, I. M., Carvalho, F., Castro, F., Fernandes, J. C., Fernandes, E. & Freitas, (2020). M. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet, v. 395, n. 10225, p. 709–733, 10.1016/S0140-6736(20)30045-3.

Rezende, L. F. M. de, Azeredo, C. M., Canella, D. S., Carmo Luiz, O. do, Levy, R. B. & Eluf-Neto, J. (2016). Coronary heart disease mortality, cardiovascular disease mortality and all-cause mortality attributable to dietary intake over 20 years in Brazil. International Journal of Cardiology, v. 217, p. 64–68, 10.1016/j.ijcard.2016.04.176.

Singhal, A., Singh, P., Fatimah, B. & Pachori, R. B. (2020). An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomedical Signal Processing and Control, v. 57, p. 101741, 10.1016/j.bspc.2019.101741.

Silva Souza, J. da. (2019). Análise de atributos de classificação para o diagnóstico de falhas em rolamentos baseados em SVM.

Xiao, R., Cui, X., Qiao, H., Zheng, X., Zhang, Y., Zhang, C. & Liu, X. (2021). Early diagnosis model of Alzheimer’s disease based on sparse logistic regression with the generalized elastic net. Biomedical Signal Processing and Control, v. 66, p. 102362, 10.1016/j.bspc.2020.102362.

Yazdani, S. & Vesin, J.-M. (2016). Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology. Digital Signal Processing, v. 56, p. 100–109.

Zidelmal, Z., Amirou, A., Ould-Abdeslam, D. & Merckle, J. (2013). ECG beat classification using a cost sensitive classifier. Computer Methods and Programs in Biomedicine, v. 111, n. 3, p. 570–577, 10.1016/j.cmpb.2013.05.011.


Métricas do artigo

Carregando Métricas ...

Metrics powered by PLOS ALM


  • Não há apontamentos.

Direitos autorais 2022 Revista Gestão & Tecnologia

Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição - NãoComercial 4.0 Internacional.