A cardiac arrhythmia monitoring platform based on feature selection and classification methods
Resumo
Heart arrhythmia, also known as irregular heartbeat, affects millions of people around the world. One of the ways to detect this cardiac dysrhythmia is by performing an electrocardiogram (ECG) exam which records the electrical activity of the heart. However, this type of exam is always interpreted by a doctor. In order to provide an alternative in heart arrhythmia diagnosis, this paper aims at developing a platform based on Internet of Things infrastructure capable of automatically monitoring and identifying cardiac arrhythmia based on feature selection and classification methods.
Referências
Ahmed, R. and Arafat, S. (2014). Cardiac arrhythmia classification using hierarchical classification model. In Computer Science and Information Technology (CSIT), 2014 6th International Conference on, pages 203–207. IEEE.
Desai, U., Martis, R. J., Nayak, C. G., Sarika, K., and Seshikala, G. (2015). Machine intelligent diagnosis of ecg for arrhythmia classification using dwt, ica and svm techniques. In India Conference (INDICON), 2015 Annual IEEE, pages 1–4. IEEE.
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182.
Ilayaraja, M. and Meyyappan, T. (2013). Mining medical data to identify frequent diseases using apriori algorithm. In Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on, pages 194–199. IEEE.
Jenny, N. Z. N., Faust, O., and Yu, W. (2014). Automated classification of normal and premature ventricular contractions in electrocardiogram signals. Journal of Medical Imaging and Health Informatics, 4(6):886–892.
Kuncheva, L. I. (2004). Combining pattern classifiers: methods and algorithms. John Wiley & Sons.
Liu, H. and Motoda, H. (2012). Feature selection for knowledge discovery and data mining, volume 454. Springer Science & Business Media.
Mitchell, T. M. (1997). Machine Learning. First edition.
Oresko, J. J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A. C. (2010). A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine, 14(3):734–740.
Pławiak, P. (2018). Novel methodology of cardiac health recognition based on ecg signals and evolutionary-neural system. Expert Systems with Applications, 92:334–349.
Soman, T. and Bobbie, P. O. (2005). Classification of arrhythmia using machine learning techniques. WSEAS Transactions on computers, 4(6):548–552.
Tsipouras, M. G., Fotiadis, D. I., and Sideris, D. (2005). An arrhythmia classification system based on the rr-interval signal. Artificial intelligence in medicine, 33(3):237–250.
Desai, U., Martis, R. J., Nayak, C. G., Sarika, K., and Seshikala, G. (2015). Machine intelligent diagnosis of ecg for arrhythmia classification using dwt, ica and svm techniques. In India Conference (INDICON), 2015 Annual IEEE, pages 1–4. IEEE.
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182.
Ilayaraja, M. and Meyyappan, T. (2013). Mining medical data to identify frequent diseases using apriori algorithm. In Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on, pages 194–199. IEEE.
Jenny, N. Z. N., Faust, O., and Yu, W. (2014). Automated classification of normal and premature ventricular contractions in electrocardiogram signals. Journal of Medical Imaging and Health Informatics, 4(6):886–892.
Kuncheva, L. I. (2004). Combining pattern classifiers: methods and algorithms. John Wiley & Sons.
Liu, H. and Motoda, H. (2012). Feature selection for knowledge discovery and data mining, volume 454. Springer Science & Business Media.
Mitchell, T. M. (1997). Machine Learning. First edition.
Oresko, J. J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A. C. (2010). A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine, 14(3):734–740.
Pławiak, P. (2018). Novel methodology of cardiac health recognition based on ecg signals and evolutionary-neural system. Expert Systems with Applications, 92:334–349.
Soman, T. and Bobbie, P. O. (2005). Classification of arrhythmia using machine learning techniques. WSEAS Transactions on computers, 4(6):548–552.
Tsipouras, M. G., Fotiadis, D. I., and Sideris, D. (2005). An arrhythmia classification system based on the rr-interval signal. Artificial intelligence in medicine, 33(3):237–250.
Publicado
22/07/2018
Como Citar
SILVA, Anderson P. N.; AQUINO-JÚNIOR, Gibeon S.; XAVIER-JÚNIOR, João C.; BARRETO, Cephas A. S..
A cardiac arrhythmia monitoring platform based on feature selection and classification methods. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 10. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 101-110.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2018.3292.
