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Research and Design of Atrial Fibrillation Early Warning Service System Based on Mobile Internet

Published: 15 October 2020 Publication History

Abstract

Atrial fibrillation is closely related to hypertension. In view of the few studies on the combination of atrial fibrillation and blood pressure, an atrial fibrillation sphygmomanometer which can measure both atrial fibrillation and blood pressure simultaneously is developed. Meanwhile, a cloud platform for atrial fibrillation early warning service and a discriminant model for atrial fibrillation are built. Supported by mobile Internet, Internet of Things and cloud computing, with atrial fibrillation and hypertension as the research object, an atrial fibrillation model was established by mixing circulating neural network (RNN) and short-term memory network (LSTM). Then we applied the model to MIT-BIH Atrial Fibrillation Database and results verified that the accuracy is as high as 98.9%. Finally, to make the system more comprehensive, we developed patient-side and physician-side APPs, including atrial fibrillation recognition, physician teleservice and health care recommendations, and doctors monitor patient synopsis in real time and provide personalized medical services.

References

[1]
Linker DT, Murphy TB, Mokdad AH. Selective screening for atrial fibrillation using multivariable risk models. Heart. 2018;104: 1492--1499.
[2]
Go AS, Reynolds K, Yang J, et al. Association of burden of atrial fibrillation with risk of ischemic stroke in adults with paroxysmal atrial fibrillation: The KP-RHYTHM Study. JAMA Cardiol. 2018;3(7):601--8.
[3]
Verdecchia, P., Angeli, F. & Reboldi, G. Hypertension and Atrial Fibrillation: Doubts and Certainties From Basic and Clinical Studies.Circ Res.122(2), 352--368 (2018).
[4]
Huxley RR, Lopez FL, Folsom AR, et al. Absolute and attributable risks of atrial fibrillation in relation to optimal and borderline risk factors: the Atherosclerosis Risk in Communities (ARIC) study[J]. Circulation, 2011, 123(14):1 501.
[5]
Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information sciences. 2017; 405: 81--90.
[6]
Feng Jizhong. Research on an ECG Monitoring and Atrial Fibrillation Early Warning System [J]. Electronic Devices, 2018, 41 (05): 1346--1349.
[7]
Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78, 659--676.
[8]
Huang Congxin, Zhang Huan, Huang Dejia, et al. Atrial fibrillation: current recognition and treatment recommendations-2018 [J]. Chinese Journal of Cardiac Pacing and Electrophysiology, 2018, 32 (4): 315--368.
[9]
Conen D, Tedrow UB, Koplan BA, et al. Influence of systolic and diastolic blood pressure on the risk of incident atrial fibrillation in women[J].Circulation, 2009, 119(16):2146.
[10]
Grundvold I, Skretteberg PT, Liestol K, et al. Upper normal blood pressures predict incident atrial fibrillation in healthy middle-agedmen: a 35-year follow-up study[J].Hypertension, 2012, 59(2):198.
[11]
Zaremba, Wojciech, Sutskever, Ilya and Vinyals, Oriol Recurrent Neural Network Regularization. (2014) cite arxiv:1409.2329.
[12]
Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Journal of Neural Computation 9(8), 1735--1780.
[13]
Faust, O.; Shenfield, A.; Kareem, M.; San, T.R.; Fujita, H.; Acharya, U.R. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput. Biol. Med. 2018, 102, 327--335.
[14]
Gers, F. A., Schmidhuber, J. & Cummins, F. A. (2000).Learning to forget: Continual prediction with lstm. Neural Computation 12, 2451--2471

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    ICMHI '20: Proceedings of the 4th International Conference on Medical and Health Informatics
    August 2020
    316 pages
    ISBN:9781450377768
    DOI:10.1145/3418094
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • University of Tsukuba: University of Tsukuba

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    Published: 15 October 2020

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    Author Tags

    1. Atrial fibrillation discrimination
    2. Atrial fibrillation sphygmomanometer
    3. LSTM
    4. RNN

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