Skip to main content
SpringerLink
Log in

Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT

  • Chapter
  • First Online:
Internet of Things for Healthcare Technologies

Part of the book series: Studies in Big Data ((SBD,volume 73))

Abstract

Real-time monitoring of life-threatening cardiovascular disease like arrhythmia using wearable sensors and Internet of things (IoT) devices paves ways to mobile health (m-health) systems. Smartphones with developed applications, wearable sensors and IoT devices are the major parts of the developed real-time arrhythmia monitoring system. In this work, an in-house round-the-clock cardiac monitoring is proposed with the use of machine learning techniques to predict the symptoms of arrhythmia by classifying the data obtained from UCI repository. The physiological signal electrocardiogram (ECG) is considered to characterize the anomalous behavior of the cardiac system. Our main novelty is to predict the symptoms of arrhythmia with the analysis and classification of data obtained from the patients using sensors or smartphones to the data classified at the repository. We establish the accuracy and efficiency of the proposed solution, by analyzing the large set of data with the field collected ECG signals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Krasteva, V., & Jekova, I. (2007). QRS template matching for recognition of ventricular ectopic beats. Annals of Biomedical Engineering, 35(12), 2065–2076.

    Article  Google Scholar 

  2. Niewolny, D. (2013). How the internet of things is revolutionizing healthcare, freescale semiconductors. In Proceedings of International Conference on Healthcare (pp. 211–219).

    Google Scholar 

  3. Hayashi, J., Kunieda, T., Cole, J., Soga, R., Hatanaka, Y., Lu, M., et al. (2004). A development of computer-aided diagnosis system using fundus images. In Proceedings Seventh International Conference on Virtual Systems and Multimedia (pp. 429–438). IEEE.

    Google Scholar 

  4. Raj, S., & Ray, K. C. (2017). ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Transactions on Instrumentation and Measurement, 66(3), 470–478.

    Article  Google Scholar 

  5. Rad, A. B., Eftestøl, T., Engan, K., Irusta, U., Kvaløy, J. T., Kramer-Johansen, J., et al. (2017). ECG-based classification of resuscitation cardiac rhythms for retrospective data analysis. IEEE Transactions on Biomedical Engineering, 64(10), 2411–2418.

    Article  Google Scholar 

  6. Luz, E. J. da S., Schwartz, W. R., Cámara-Chávez, G., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine, 127, 144–164.

    Google Scholar 

  7. Mohapatra, S. K., & Mohanty, M. N. (2018, September). Analysis of resampling method for arrhythmia classification using random forest classifier with selected features. In 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) (pp. 495–499). IEEE.

    Google Scholar 

  8. Ozcift, A., & Gulten, A. (2011). Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer Methods and Programs in Biomedicine, 104(3), 443–451.

    Article  Google Scholar 

  9. Escalona-Morán, M. A., Soriano, M. C., Fischer, I., & Mirasso, C. R. (2014). Electrocardiogram classification using reservoir computing with logistic regression. IEEE Journal of Biomedical and Health Informatics, 19(3), 892–898.

    Article  Google Scholar 

  10. Gutta, S., & Cheng, Q. (2015). Joint feature extraction and classifier design for ECG-based biometric recognition. IEEE Journal of Biomedical and Health Informatics, 20(2), 460–468.

    Article  Google Scholar 

  11. Shadmand, S., & Mashoufi, B. (2016). A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control, 25, 12–23.

    Article  Google Scholar 

  12. Ince, T., Kiranyaz, S., & Gabbouj, M. (2009). A generic and robust system for automated patient-specific classification of ECG signals. IEEE Transactions on Biomedical Engineering, 56(5), 1415–1426.

    Article  Google Scholar 

  13. Gao, D., Madden, M., Chambers, D., & Lyons, G. (2005, July). Bayesian ANN classifier for ECG arrhythmia diagnostic system: A comparison study. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 (Vol. 4, pp. 2383–2388). IEEE.

    Google Scholar 

  14. Xu, S., Mak, M. W., & Cheung, C. C. (2017, July). Deep neural networks versus support vector machines for ECG arrhythmia classification. In 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 127–132). IEEE.

    Google Scholar 

  15. Tracey, B. H., & Miller, E. L. (2012). Nonlocal means denoising of ECG signals. IEEE Transactions on Biomedical Engineering, 59(9), 2383–2386.

    Article  Google Scholar 

  16. Jain, S. K., & Bhaumik, B. (2016). An energy efficient ECG signal processor detecting cardiovascular diseases on smartphone. IEEE Transactions on Biomedical Circuits and Systems, 11(2), 314–323.

    Article  Google Scholar 

  17. Li, P., Wang, Y., He, J., Wang, L., Tian, Y., Zhou, T. S., et al. (2016). High-performance personalized heartbeat classification model for long-term ECG signal. IEEE Transactions on Biomedical Engineering, 64(1), 78–86.

    Article  Google Scholar 

  18. Satija, U., Ramkumar, B., & Manikandan, M. S. (2017). Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 722–732.

    Article  Google Scholar 

  19. Spanó, E., Di Pascoli, S., & Iannaccone, G. (2016). Low-power wearable ECG monitoring system for multiple-patient remote monitoring. IEEE Sensors Journal, 16(13), 5452–5462.

    Article  Google Scholar 

  20. Roonizi, E. K., & Sassi, R. (2015). A signal decomposition model-based Bayesian framework for ECG components separation. IEEE Transactions on Signal Processing, 64(3), 665–674.

    Article  MathSciNet  Google Scholar 

  21. American Heart Association. (2016). Cardiac arrest statistics. http://cpr.heart.org/AHAECC/CPRAndECC/General/UCM_477263_Cardiac-Arrest-Statistics.jsp. Accessed December 2, 2016.

  22. Chen, X., Xu, D., Zhang, G., & Mukkamala, R. (2009, September). Forecasting acute hypotensive episodes in intensive care patients based on a peripheral arterial blood pressure waveform. In 2009 36th Annual Computers in Cardiology Conference (CinC) (pp. 545–548). IEEE.

    Google Scholar 

  23. Deshmane, A. V. (2009). False arrhythmia alarm suppression using ECG, ABP, and photoplethysmogram (Doctoral dissertation, Massachusetts Institute of Technology).

    Google Scholar 

  24. Ganeshapillai, G., & Guttag, J. V. (2011). Weighted time warping for temporal segmentation of multi-parameter physiological signals. In BIOSIGNALS 2011.

    Google Scholar 

  25. Banerjee, R., Ghose, A., Choudhury, A. D., Sinha, A., & Pal, A. (2015, April). Noise cleaning and Gaussian modeling of smart phone photoplethysmogram to improve blood pressure estimation. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 967–971). IEEE.

    Google Scholar 

  26. Sachpazidis, I., Stassinakis, A., Memos, D., Fragou, S., Nachamoulis, S., Vamvatsikos, A., et al. (2002). HOME ein neues Eu-projekt zum Tele Home Care. Biomedizinische Technik/Biomedical Engineering, 47(s1b), 970–972.

    Article  Google Scholar 

  27. Fensli, R., Gunnarson, E., & Gundersen, T. (2005, June). A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation. In 18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05) (pp. 407–412). IEEE.

    Google Scholar 

  28. Guvenir, H. A., Acar, B., Demiroz, G., & Cekin, A. (1997, September). A supervised machine learning algorithm for arrhythmia analysis. In Computers in Cardiology 1997 (pp. 433–436). IEEE.

    Google Scholar 

  29. Torfs, T., Leonov, V., Van Hoof, C., & Gyselinckx, B. (2006, October). Body-heat powered autonomous pulse oximeter. In SENSORS, 2006 IEEE (pp. 427–430). IEEE.

    Google Scholar 

  30. Chakraborty, C. (2019). Computational approach for chronic wound tissue characterization. Informatics in Medicine Unlocked, 17, 1–10.

    Google Scholar 

  31. Chakraborty, C. (2019). Mobile health (m-Health) for tele-wound monitoring. In Mobile health applications for quality healthcare delivery (Ch. 5, pp. 98–116). Hershey, PA: IGI. ISBN: 9781522580218. https://doi.org/10.4018/978-1-5225-8021-8.ch005.

  32. Martin, T., Jovanov, E., & Raskovic, D. (2000, October). Issues in wearable computing for medical monitoring applications: A case study of a wearable ECG monitoring device. In Digest of Papers. Fourth International Symposium on Wearable Computers (pp. 43–49). IEEE.

    Google Scholar 

  33. Li, M., Yu, S., Zheng, Y., Ren, K., & Lou, W. (2012). Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Transactions on Parallel and Distributed Systems, 24(1), 131–143.

    Article  Google Scholar 

  34. Ruj, S., Stojmenovic, M., & Nayak, A. (2012, May). Privacy preserving access control with authentication for securing data in clouds. In 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012) (pp. 556–563). IEEE.

    Google Scholar 

  35. Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Cheshire, CT: Graphics Press.

    Google Scholar 

  36. Healey, C. G. (1996, October). Choosing effective colours for data visualization. In Proceedings of Seventh Annual IEEE Visualization’96 (pp. 263–270). IEEE.

    Google Scholar 

  37. Devadharshini, M. S., Heena Firdaus, A. S., Sree Ranjani, R., & Devarajan, N. (2019). Real time arrhythmia monitoring with machine learning classification and IoT. In 2019 Fifth International Conference on Data Science and Engineering (ICDSE).

    Google Scholar 

  38. Uyar, A., & Gurgen, F. (2007, September). Arrhythmia classification using serial fusion of support vector machines and logistic regression. In 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (pp. 560–565). IEEE.

    Google Scholar 

  39. Aliferis, C. F., Tsamardinos, I., & Statnikov, A. (2003). HITON: A novel Markov Blanket algorithm for optimal variable selection. In AMIA Annual Symposium Proceedings (Vol. 2003, p. 21). American Medical Informatics Association.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. RISE Lab, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India

    Rajendran Sree Ranjani

Authors
  1. Rajendran Sree Ranjani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajendran Sree Ranjani .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Chinmay Chakraborty

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Amit Banerjee

  3. Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, India

    Maheshkumar H. Kolekar

  4. Department of Computer Information Systems, Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  5. Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Japan

    Basabi Chakraborty

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sree Ranjani, R. (2021). Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT. In: Chakraborty, C., Banerjee, A., Kolekar, M., Garg, L., Chakraborty, B. (eds) Internet of Things for Healthcare Technologies. Studies in Big Data, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-15-4112-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation