Unsupervised analysis applied to the detection cardiac arrhythmias


  • Mónica Moreno-Revelo Universidad de Nariño
  • Sandra Patascoy-Botina Universidad de Nariño
  • Andrés Pantoja-Buchelli Universidad de Nariño
  • Javier Revelo Fuelagán Universidad de Nariño
  • José Rodríguez-Sotelo Universidad autónoma de Manizales
  • Santiago Murillo-Rendón Universidad autónoma de Manizales
  • Diego Peluffo-Ordoñez Universidad Técnica del Norte




Clustering, Segment-based methodology, Initialization, Quality measures.


An arrhythmia is a pathology that consists on altering the heartbeat. Although, the 12-lead electrocardiogram allows evaluation of the electrical behavior from heart to determine certain pathologies, there are some arrhythmias that are difficult to detect with this type of electrocardiography. In this sense, it is necessary the use of the Holter monitor because it facilitates the records of the heart electrical activity for long periods of time, it is usually 24 up to 48 hours. Due to the extension of the records provided by the monitor, it is common to use computational systems to evaluate diagnostic and morphological features of the beats in order to determine if there is any type of abnormality. These computational systems can be based on supervised or unsupervised pattern recognition techniques, however considering that the first option requires a visual inspection about the large number of beats present in a Holter record, it is an arduous task, as well as it involves monetary costs. Consequently, throughout this paper we present the design of a complete system for the identification of arrhythmias in Holter records using unsupervised pattern recognition techniques. The proposed system involves stages of preprocessing of the signal, segmentation and characterization of beats, as well as feature selection and clustering. In this case, the technique k-means is used. These steps are applied within the framework of a segment-based methodology that improves the detection of minority classes. Additionally, initialization criteria are considered, which allow to enhance quality measures, especially sensitivity. As a result, it is determined that using k-means with the max-min initialization and a number of groups equal to 12, it is possible to obtain the best results, with values of: 99.36%, 91.31% and 99.16% for accuracy, sensitivity and specificity, respectively.



Download data is not yet available.


Aggarwal, C. C., & Zhai, C. (2012). Mining text data. Springer Science & Business Media.
Aldahdooh, R. T., & Ashour, W. (2013). DIMK-means" Distance-based Initialization Method for K-means Clustering Algorithm". International Journal of Intelligent Systems and Applications, 5(2), 41.
Anderberg, M. R. (2014). Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks (Vol. 19). Academic press.
Balachandran, A., Ganesan, M., & Sumesh, E. P. (2014, March). Daubechies algorithm for highly accurate ECG feature extraction. In Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on (pp. 1-5). IEEE.
Bhateja, V., Urooj, S., Mehrotra, R., Verma, R., Lay-Ekuakille, A., & Verma, V. D. (2013, December). A composite wavelets and morphology approach for ECG noise filtering. In International Conference on Pattern Recognition and Machine Intelligence (pp. 361-366). Springer Berlin Heidelberg.
Byrne, C. L. (2014). Signal Processing: a mathematical approach. CRC Press.
Carreiras, C., Lourenço, A., Aidos, H., da Silva, H. P., & Fred, A. L. (2016). Unsupervised Analysis of Morphological ECG Features for Attention Detection. In Computational Intelligence (pp. 437-453). Springer International Publishing.
Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200-210.
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Chen, Y. H., & Yu, S. N. (2012). Selection of effective features for ECG beat recognition based on nonlinear correlations. Artificial intelligence in medicine, 54(1), 43-52.
Chung, E. K. (2013). Ambulatory electrocardiography: holter monitor electrocardiography. Springer Science & Business Media.
Jannah, N., & Hadjiloucas, S. (2015, December). Detection of ECG arrhythmia conditions using CSVM and MSVM classifiers. In 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1-2). IEEE.
Kavitha, B., Karthikeyan, S., & Chitra, B. (2010). Efficient intrusion detection with reduced dimension using data mining classification methods and their performance comparison. In Information Processing and Management (pp. 96-101). Springer Berlin Heidelberg.
Khan, T. T., Sultana, N., Reza, R. B., & Mostafa, R. (2015, May). ECG feature extraction in temporal domain and detection of various heart conditions. In Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference on (pp. 1-6). IEEE.
Martis, R. J., Acharya, U. R., & Min, L. C. (2013). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437-448.
Peshave, J. D., & Shastri, R. (2014, April). Feature extraction of ECG signal. In Communications and Signal Processing (ICCSP), 2014 International Conference on (pp. 1864-1868). IEEE.
Revathi, S., & Nalini, D. T. (2013). Performance comparison of various clustering algorithm. International Journal of Advanced Research in Computer Science and Software Engineering, 3(2).
Rodriguez, C. A., Gallego, J. H., Mora, I. D., Orozco-Duque, A., & Bustamante, J. (2014). Clasificación de latidos de contracción ventricular prematura basados en métodos de aprendizaje no supervisado. Revista Ingeniería Biomédica, 8(15), 51-58.
Rodríguez-Sotelo, J. L., Peluffo-Ordoñez, D., & Dominguez, G. C. (2015, January). Segment clustering methodology for unsupervised Holter recordings analysis. In Tenth International Symposium on Medical Information Processing and Analysis (pp. 92870M-92870M). International Society for Optics and Photonics.
Senapati, M. K., Senapati, M., & Maka, S. (2014, August). Cardiac Arrhythmia Classification of ECG Signal Using Morphology and Heart Beat Rate. In Advances in Computing and Communications (ICACC), 2014 Fourth International Conference on (pp. 60-63). IEEE.
Tzortzis, G., & Likas, A. (2014). The MinMax k-Means clustering algorithm. Pattern Recognition, 47(7), 2505-2516.
Wang, J., Wang, J., Ke, Q., Zeng, G., & Li, S. (2015). Fast approximate k-means via cluster closures. In Multimedia Data Mining and Analytics (pp. 373-395). Springer International Publishing.
Xie, X. Q., Wang, L. H., Jiang, S. Y., Lee, S. Y., Lin, K. H., Wang, X. K., ... & Deng, N. (2015, October). An ECG feature extraction with wavelet algorithm for personal healthcare. In Bioelectronics and Bioinformatics (ISBB), 2015 International Symposium on (pp. 128-131). IEEE.
Ye, C., Kumar, B. V., & Coimbra, M. T. (2012). Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Transactions on Biomedical Engineering, 59(10), 2930-2941.



How to Cite

Moreno-Revelo, M., Patascoy-Botina, S., Pantoja-Buchelli, A., Revelo Fuelagán, J., Rodríguez-Sotelo, J., Murillo-Rendón, S., & Peluffo-Ordoñez, D. (2017). Unsupervised analysis applied to the detection cardiac arrhythmias. Enfoque UTE, 8(1), pp. 257 - 272. https://doi.org/10.29019/enfoqueute.v8n1.125