Abstract
Arrhythmia related disorders are one of the leading causes of cardiac deaths in the world. Previous studies have shown that Arrhythmia can further lead to major cardiac diseases like the Sudden Cardiac Death (SCD) syndrome. The difficulty in detecting Arrhythmia in the early stages often results in poor prognosis and presents the need for a costefficient diagnostic device. To this end, we propose a realtime portable ECG device with special emphasis on Arrhythmia detection and classification. The device is centered on a Raspberry Pi 3 (RasPi) module. RasPi with its signal processing and wireless transfer capabilities acts like an adapter between the sensors and a personalized mobile device application that is used for tracking the ECG. A highly sensitive peak detection algorithm was used by RasPi to detect and extract features from the ECG signals at real time. The peak detection algorithm was tested on the standard MITBIH arrhythmia database and reported an accuracy of greater than 95%. Hence, we propose a novel low cost approach towards arrhythmia monitoring and detection with wide applications in mobile health systems.
Keywords
- Arrhythmia
- Portable
- Costeffective
- Wireless communication
- Mobile health
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References
Krasteva, V., Jekova, I.: QRS template matching for recognition of ventricular ectopic beats. Ann. Biomed. Eng. 5(12), 2065–2076 (2007)
Mehra, R.: Global public health problem of sudden cardiac death. J. Electrocardiol. 40(6), S118–S122 (2007)
Silva, I., Moody, G.B., Celi, L.: Improving the quality of ECGs collected using mobile phones: the physionet/computing in cardiology challenge 2011. In: 2011 Computing in Cardiology. IEEE (2011)
Eskofier, B., Hoenig, F., Kuehner, P.: Classification of perceived running fatigue in digital sports. In: ICPR (2008)
Gradl, S., et al.: Realtime ECG monitoring and arrhythmia detection using Android based mobile devices. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2012)
Hamilton, P.S., Tompkins, W.J.: Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 12, 1157–1165 (1986)
Gothwal, H., Kedawat, S., Kumar, R.: Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. J. Biomed. Sci. Eng. 4(04), 289 (2011)
Goldenberg, I., Zareba, W., Moss, A.J.: Long QT syndrome. Curr. Probl. Cardiol. 33(11), 629–694 (2008)
Zhao, C.W., Jegatheesan, J., Loon, S.C.: Exploring IOT application using Raspberry Pi. Int. J. Comput. Netw. Appl. 2(1), 27–34 (2015)
http://www.analog.com/media/en/technical-documentation/data-sheets/AD8232.pdf
Naaz, A., Singh, S.: QRS complex detection and ST segmentation of ECG signal using wavelet transform. Int. J. Res. Advent Technol. 3(6), June 2015
Schwartz, P.J., Periti, M., Malliani, A.: The long QT syndrome. Am. Heart J. 89(3), 378–390 (1975)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH Arrhythmia database. IEEE Eng. Med. Biol. 20(3), 45–50 (2001)
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Valliappan, C.A., Balaji, A., Thandayam, S.R., Dhingra, P., Baths, V. (2017). A Portable Real Time ECG Device for Arrhythmia Detection Using Raspberry Pi. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_24
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DOI: https://doi.org/10.1007/978-3-319-58877-3_24
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