Abstract
Nowadays a personalized approach is being giving to health care concerning the prevention of diseases, improving diagnosis and treatment of patients, for this, equipment to measure ambulatory vital signs are used, allowing to get large volumes of information. Nevertheless, the obtained information from ambulatory electrocardiography has no largely clinical validity because it is contaminated with motion artifacts, for this reason, it is necessary to determine what information is useful and what information can be ruled out. This paper presents a comparison between two different classification methods of electrocardiography signals: Artificial Neural Networks and Support Vector Machines. Database includes electrocardiography signals of volunteers and some important features of these signals are extracted to train both classification methods. Also, performance of methods is assessed verifying the generalization capabilities. The best performance was presented by the Radial Basis Function kernel.
Preview
Unable to display preview. Download preview PDF.
References
Sarraf Elie. Quality of Life IEEE PULSE. 2010;1:45–50.
Darlenski Razvigor Borislavov, Neykov Neyko Valentinov, Vlahov Vitan Dakov, Tsankov Nikolaï Konstantinov. Evidence-based medicine: Facts and controversies Clinics in Dermatology. 2010;28:553–557.
Golubnitschaja Olga, Kinkorova Judita, Costigliola Vincenzo. Predictive, Preventive and Personalised Medicine as the hardcore of ‘Horizon 2020’: EPMA position paper. The EPMA journal. 2014;5:1–29.
Pantelopoulos Alexandros, Bourbakis Nikolaos. A health prognosis wearable system with learning capabilities using NNs Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. 2009:243–247.
Jung Sang-Joong, Kwon Tae-Ha, Chung Wan-Young. A new approach to design ambient sensor network for real time healthcare monitoring system 2009 IEEE Sensors. 2009:576–580.
Pandian P. S., Mohanavelu K., Safeer K. P., et al. Smart Vest: Wearable multi-parameter remote physiological monitoring system Medical Engineering and Physics. 2008;30:466–477.
Vullings E, Krijgsman A J, Verbruggen H B. Validating ECG Signals using a single-layer ANN 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam 1996. 1996;18:919–920.
Maglaveras Nicos, Stamkopoulos Telemachos, Diamantaras Konstantinos, Pappas Costas, Strintzis Michael. ECG pattern recognition and classification using non-linear transformations and neural networks: A review International Journal of Medical Informatics. 1998;52:191–208.
Vargas Fabian, Lettnin Djones, De Castro Maria Cristina Felippetto, Macarthy Marcello. Electrocardiogram pattern recognition by means of MLP network and PCA: A case study on equal amount of input signal types Proceedings - Brazilian Symposium on Neural Networks, SBRN. 2002;2002-Janua:200–205.
Han Liang, Pu Xiujuan, Zhou Na, Zhang Bowei, Jiang Wenhao. Extraction of Fetal Electrocardiogram Using Online Least Squares Support Vector Machines Journal of Information & Computational Science. 2011;8:2045–2057.
Liu Bo, Hao Zhifeng, Tsang Eric C C. Nesting one-against-one algorithm based on SVMs for pattern classification IEEE Transactions on Neural Networks. 2008;19:2044–2052.
Barnett Adrian G, Wolff Rodney C. A time-domain test for some types of nonlinearity IEEE Transactions on Signal Processing. 2005;53:26–33.
Kauffmann F., Maison-Blanche P., Cauchemez B., et al. A study of non stationary phenomena of HRV during 24-hour ECG ambulatory monitoring Computers in Cardiology, 1988.. 1988;2:303–306.
Zheng Qian, Chen Chao, Li Zhinan, et al. A novel multi-resolution SVM (MR-SVM) algorithm to detect ECG signal anomaly in WE-CARE project ISSNIP Biosignals and Biorobotics Conference, BRC. 2013.
Kohli Narendra, Verma Nishchal K., Roy Abhishek. SVM based methods for arrhythmia classification in ECG 2010 International Conference on Computer and Communication Technology, ICCCT-2010. 2010;10:486–490.
Ding Zining, Wang Feng, Zhou Ping. Fetal ECG extraction based on different kernel functions of SVM 2011 3rd International Conference on Computer Research and Development. 2011;3:205–208.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Castaño, F.A., Hernández, A.M. (2017). Motion Artifacts Recognition in Electrocardiographic Signals through Artificial Neural Networks and Support Vector Machines for Personalized Health Monitoring. In: Torres, I., Bustamante, J., Sierra, D. (eds) VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016. IFMBE Proceedings, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-10-4086-3_107
Download citation
DOI: https://doi.org/10.1007/978-981-10-4086-3_107
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4085-6
Online ISBN: 978-981-10-4086-3
eBook Packages: EngineeringEngineering (R0)