Cardiac arrhythmia diagnosis applying machine learning techniques
Diagnóstico de Arritmias cardíacas aplicando técnicas de aprendizado de máquina
DOI:
https://doi.org/10.34119/bjhrv5n5-238Keywords:
Cardiac arrhythmia, machine learning, support vector machine, nearest neighbourAbstract
Cardiac arrhythmia affects millions of people worldwide. Although commonly occurring, identifying and correctly diagnosing is not a simple task. In this context, this paper presents a study on the application of Machine Learning to the identification and diagnosis of cardiac arrhythmias. Classifiers were obtained using the k-NN and SVM algorithms, and tests were performed using data from the Arrhythmia dataset, which consists of information obtained from patients' ECG examinations, as well as information related to their lifestyles. Three tests were performed; in the first one, the ability of classifiers to identify whether or not an arrhythmia episode occurred. In the second one, the performance of the classifiers in the identification of the arrhythmia type was verified, and in the third one, the investigation was performed considering the gender of the individuals. The results indicate that the use of Machine Learning may, in fact, assist specialists in the diagnosis of arrhythmias. In all tests k-NN presented better performance when compared to SVM. The best result among all tests was obtained by gender classification, where k-NN presented a accuracy of 94.03% in identifying arrhythmia occurrences in female patients.
References
BISHOP, C. M. Pattern recognition and machine learning. [S.l.]: springer, 2006.
BRIDGELALL, R. Introduction to support vector machines. <https://www.ugpti.org/smartse/resources/downloads/support-vector-machines.pdf>. Acessado em: 2019-10-13.
CUI, S. et al. Plasma phospholipids and sphingolipids identify stent restenosis after percutaneous coronary intervention. JACC: Cardiovascular Interventions, JACC: Cardiovascular Interventions, v. 10, n. 13, p. 1307–1316, 2017.
CUNNINGHAM, P.; DELANY, S. k-Nearest Neighbour Classifiers. Mult Classif Syst, 04 2007.
DUA, D.; GRAFF, C. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. 2019. Disponível em: <http://archive.ics.uci.edu/ml>.
GUTIÉRREZ-GNECCHI, J. A. et al. Dsp-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomedical Signal Processing and Control, v. 32, p. 44 – 56, 2017. ISSN 1746-8094. Disponível em: <http://www.sciencedirect.com/science/article/pii>.
HALL, J. E. Guyton and Hall textbook of medical physiology e-Book. [S.l.]: Elsevier Health Sciences, 2015.
KAUR, M.; ARORA, A.S. Classification of arrhythmias with LDA and ANN using orthogonal rotations for feature reduction. International Journal
of Computer Science, v. 9, n 4. p 1694-0814, 2012.
KOHLI, N.; VERMA, N. K. Arrhythmia classification using svm with selected features. International Journal of Engineering, Science and Technology, MultiCraft Limited, v. 3, n. 8, p. 122–131, 2011.
MARSLAND, S. Machine learning: an algorithmic perspective. [S.l.]: Chapman and Hall/CRC, 2014.
MUSTAQEEM, A.; ANWAR, S. M.; MAJID, M.. Multiclass classification of cardiac arrhythmia using improved feature selection and svm invariants. Computational and mathematical methods in medicine, Hindawi, v. 2018, 2018.
OUELLI, A. et al. Electrocardiogram Features Extraction and Classification for Arrhythmia Detection. Journal of Multidisciplinary Engineering Science and Technology (JMEST), v. 2, p 461 – 464, 2015.
PARK, J.; KANG, K.. PcHD: Personalized Classification of Heartbeat Types Using a Decision Tree. Computers in Biology and Medicine, v. 54, p. 79 – 88, 2014. ISSN 0010-4825.
PASTORE, C.A. et al. III diretrizes da sociedade brasileira de cardiologia sobre análise e emissão de laudos eletrocardiográficos. Arquivos Brasileiros de Cardiologia, SciELO Brasil, v. 106, n. 4, p. 1–23, 2016.
POLAT, K.; GÜNES, S. Detection of ecg arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine. Applied Mathematics and Computation, Elsevier, v. 186, n. 1, p. 898–906, 2007.
THALER, M. S. ECG Essencial - Eletrocardiograma na Prática Diária. [S.l.]: Artmed, 2013.
VAPNIK, V. N.; CHERVONENKIS, A. Theory of pattern recognition: Statistical problems of learning. Moscow: Nauka, 1963.
WOSIAK, A. Principal component analysis based on data characteristics for dimensionality reduction of ecg recordings in arrhythmia classification. Open Physics, De Gruyter, v. 17, n. 1, p. 489–496, 2019.
ZAKKA, K. A Complete Guide to K-Nearest-Neighbors with Applications in Python and R. 2016. <https://kevinzakka.github.io/2016/07/13>. Acessado em: 2019-07-31.
ZHANG, L. et al. Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots. Physiological Measurement, v. 36, p 283 – 301. 2015.
