SpringerLink
Log in

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The heart rate variability signal is a valuable tool for cardiovascular system diagnostics. Processing this signal detects arrhythmia during long-term cardiac monitoring. It is also analyzed to recognize abnormalities within the autonomic nervous system. Processing this signal helps in detecting various pathologies, such as atrial fibrillation (AF), supraventricular tachycardia (SVT), and congestive heart failure (CHF). As a beneficial alternative to the commonly used HRV spectrum analysis, quadratic time–frequency analysis of HRV signals could be helpful in heart pathology detection. Indeed, in this study, we have created a client-server paradigm deployed as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This platform detects arrhythmia in real-time by deploying time–frequency analysis, feature extraction, feature selection, and classification of Heart Rate Variability (HRV) signals. We gathered all these functionalities in a Graphical User Interface (GUI) in addition to data acquisition. As a client, a Raspberry Pi Zero ensures data acquisition and connects to a server over TCP/IP that involves a 4G/3G connection encrypted through the transport layer security (TLS). This telemedical tool continuously monitors the heart rate variability. In the case of an alarm, medical professionals may immediately interact with their patients in the hospital or at home.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availability Statement

The data used in the current study are available in the PhysioNet repository, managed by the MIT Laboratory for Computational Physiology. For more information about the data used in this study, visit https://physionet.org.

Abbreviations

ADC:

Analog–Digital converter

CPU:

Central Processing Unit

ECG:

Electrocardiogram

FS:

Features selected

FSASL:

Feature selection with adaptive structure learning

FSM:

Features selection methods

HRV:

Heart rate variability

IoT:

Internet of Things

IP:

Internet protocol

MI:

Mutual information

NF:

Number of features

SPI:

Serial peripheral interface

SVM:

Support Vector Machine

TCP:

Transmission control protocol

TF:

Time–frequency

TLS:

Transport layer security

References

  1. Abe S (2005) Support vector machines for pattern classification. Springer, Berlin

    MATH  Google Scholar 

  2. Abualsaud K, Chowdhury ME, Gehani A, Yaacoub E, Khattab T, Hammad J (2020) A new wearable ECG monitor evaluation and experimental analysis: proof of concept. In: 2020 International Wireless Communications and Mobile Computing, IWCMC 2020, Institute of Electrical and Electronics Engineers Inc., pp 1885–1890. https://doi.org/10.1109/IWCMC48107.2020.9148191

  3. Anderson R, Jonsson P, Sandsten M (2019) Stochastic modeling and optimal time-frequency estimation of task-related HRV. Appl Sci 9(23):5154

    Article  Google Scholar 

  4. Ardissino M, Nicolaou N, Vizcaychipi M (2019) Non-invasive real-time autonomic function characterization during surgery via continuous poincaré quantification of heart rate variability. J Clin Monit Comput 33(4):627–635

    Article  Google Scholar 

  5. Atalar AC, Savrun FK, Yeni SN (2019) Autonomic dysfunction during the interictal period: an electrophysiologic study. Neurol Sci Neurophysiol 36(1):9–15

    Article  Google Scholar 

  6. Barkat B, Boashash B (2001) A high-resolution quadratic time-frequency distribution for multicomponent signals analysis. IEEE Trans Signal Process 49(10):2232–2239. https://doi.org/10.1109/78.950779

    Article  MATH  Google Scholar 

  7. Boashash B (2015) Time-frequency signal analysis and processing: a comprehensive reference. Academic Press, Cambridge

    Google Scholar 

  8. Boashash B (2016) Chapter 12—detection, classification, and estimation in the (t, f) domain. Time–frequency signal analysis and processing, 2nd edn. Academic Press, Cambridge, pp 693–743

    Google Scholar 

  9. Boashash B, Ouelha S (2017) An improved design of high-resolution quadratic time-frequency distributions for the analysis of nonstationary multicomponent signals using directional compact Kernels. IEEE Trans Signal Process 65(10):2701–2713. https://doi.org/10.1109/TSP.2017.2669899

    Article  MathSciNet  MATH  Google Scholar 

  10. Boashash B, Azemi G, Ali Khan N (2015) Principles of time-frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection. Pattern Recognit 48(3):616–627. https://doi.org/10.1016/J.PATCOG.2014.08.016

    Article  Google Scholar 

  11. Chamasemani FF, Singh YP (2011) Multi-class support vector machine (svm) classifiers—an application in hypothyroid detection and classification. In: 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications, pp 351–356. https://doi.org/10.1109/BIC-TA.2011.51

  12. Chang RCH, Chen HL, Lin CH, Lin KH (2018) Design of a low-complexity real-time arrhythmia detection system. J Signal Process Syst 90(1):145–156. https://doi.org/10.1007/s11265-017-1221-2

    Article  Google Scholar 

  13. Chen YC, Hsiao CC, Zheng WD, Lee RG, Lin R (2019) Artificial neural networks-based classification of emotions using wristband heart rate monitor data. Medicine 98(33):e16863. https://doi.org/10.1097/MD.0000000000016863

    Article  Google Scholar 

  14. Clark N, Sandor E, Walden C, Ahn IS, Lu Y (2019) A wearable ECG monitoring system for real-time arrhythmia detection. In: Midwest Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc., vol 2018-August, pp 787–790. https://doi.org/10.1109/MWSCAS.2018.8624097

  15. Devi RL, Kalaivani V (2020) Machine learning and IoT-based cardiac arrhythmia diagnosis using statistical and dynamic features of ECG. J Supercomput 76(9):6533–6544. https://doi.org/10.1007/s11227-019-02873-y

    Article  Google Scholar 

  16. Dorey TW, Moghtadaei M, Rose RA (2020) Altered heart rate variability in angiotensin II–mediated hypertension is associated with impaired autonomic nervous system signaling and intrinsic sinoatrial node dysfunction. Heart Rhythm 17(8):1360–1370. https://doi.org/10.1016/j.hrthm.2020.03.014

    Article  Google Scholar 

  17. Du L, Shen YD (2015) Unsupervised feature selection with adaptive structure learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, vol 2015-August, pp 209–218. https://doi.org/10.1145/2783258.2783345, arxiv:1504.00736

  18. Geng DY, Zhao J, Wang CX, Ning Q (2020) A decision support system for automatic sleep staging from hrv using wavelet packet decomposition and energy features. Biomed Signal Process Control 56:101722

    Article  Google Scholar 

  19. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  Google Scholar 

  20. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  21. He J, Jiang N (2019) Optimizing probability threshold of convolution neural network to improve hrv-based acute stress detection performance. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 5318–5321

  22. Hong JH, Cho SB (2008) A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification. Neurocomputing 71(16–18):3275–3281. https://doi.org/10.1016/j.neucom.2008.04.033

    Article  Google Scholar 

  23. Hossain MS, Muhammad G (2016) Cloud-assisted Industrial Internet of Things (IIoT)—enabled framework for health monitoring. Comput Netw 101:192–202. https://doi.org/10.1016/j.comnet.2016.01.009

    Article  Google Scholar 

  24. Joshi R, Kommers D, Guo C, Bikker JW, Feijs L, van Pul C, Andriessen P (2019) Statistical modeling of heart rate variability to unravel the factors affecting autonomic regulation in preterm infants. Sci Rep 9(1):1–9

    Article  Google Scholar 

  25. Kim J, Kim Y, Zewge NS, Kim JH (2019) A robust client–server architecture for map information processing and transmission for distributed visual SLAM. In: 2019 7th International Conference on Robot Intelligence Technology and Applications, RiTA 2019, Institute of Electrical and Electronics Engineers Inc., pp 99–105. https://doi.org/10.1109/RITAPP.2019.8932869

  26. Kobayashi M, Sun G, Shinba T, Matsui T, Kirimoto T (2019) Development of a mental disorder screening system using support vector machine for classification of heart rate variability measured from single-lead electrocardiography. In: 2019 IEEE sensors applications symposium (SAS), IEEE, pp 1–6

  27. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324. https://doi.org/10.1016/S0004-3702(97)00043-X

    Article  MATH  Google Scholar 

  28. Löfhede J, Thordstein M, Löfgren N, Flisberg A, Rosa-Zurera M, Kjellmer I, Lindecrantz K (2010) Automatic classification of background EEG activity in healthy and sick neonates. J Neural Eng 7(1):016007. https://doi.org/10.1088/1741-2560/7/1/016007

    Article  Google Scholar 

  29. Mamouni El Mamoun SK, Mahmoud Z (2019) Svm model selection using pso for learning handwritten arabic characters. Comput Mater Contin 61(3):995–1008

    Google Scholar 

  30. Pan J, Tompkins WJ (1985) A real-time qrs detection algorithm. IEEE Trans Biomed Eng 3:230–236

    Article  Google Scholar 

  31. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  32. Poddar M, Birajdar AC, Virmani J et al (2019) Automated classification of hypertension and coronary artery disease patients by PNN, KNN, and SCM classifiers using HRV analysis. Machine learning in bio-signal analysis and diagnostic imaging. Elsevier, Amsterdam, pp 99–125

    Chapter  Google Scholar 

  33. Pukkawanna S, Blanc G, Garcia-Alfaro J, Kadobayashi Y, Debar H (2014) Classification of SSL servers based on their SSL handshake for automated security assessment. In: 2014 Third international workshop on building analysis datasets and gathering experience returns for security (BADGERS), pp 30–39. https://doi.org/10.1109/BADGERS.2014.10

  34. Seong HM, Lee JS, Shin TM, Kim WS, Yoon YR, Yoon YR (2004) The analysis of mental stress using time-frequency distribution of heart rate variability signal. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference, vol 1, pp 283–285. https://doi.org/10.1109/IEMBS.2004.1403147

  35. Singh R, Saini B, Sunkaria R (2019) Power spectral study of heart rate variability time series by the adaptive modified continuous morlet wavelet transform. Res Rev J Med Sci Technol 6(2):5–20

    Google Scholar 

  36. Stradolini F, Lavalle E, De Micheli G, Ros PM, Demarchi D, Carrara S (2017) Paradigm-shifting players for IoT: Smart-watches for intensive care monitoring. In: Lecture notes of the institute for computer sciences, social-informatics and telecommunications engineering, LNICST, vol 192, Springer, Verlag, pp 71–78. https://doi.org/10.1007/978-3-319-58877-3_9

  37. Stradolini F, Tamburrano N, Modoux T, Tuoheti A, Demarchi D, Carrara S (2018) IoT for telemedicine practices enabled by an android\(^{\rm TM}\) application with cloud system integration. In: Proceedings - IEEE international symposium on circuits and systems, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISCAS.2018.8351871

  38. Thilakanathan D, Chen S, Nepal S, Calvo R, Alem L (2014) A platform for secure monitoring and sharing of generic health data in the Cloud. Future Gener Comput Syst 35:102–113. https://doi.org/10.1016/j.future.2013.09.011

    Article  Google Scholar 

  39. Valliappan CA, Balaji A, Thandayam SR, 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

    Chapter  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Directorate General of Scientific Research and Technological Development (Direction Générale de la Recherche Scientifique et du Développement Technologique, DGRSDT, URL:www.dgrsdt.dz, Algeria) for the financial assistance towards this research.

Author information

Authors and Affiliations

  1. Laboratory of Biomedical Engineering, Department of Biomedical Engineering, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria

    Ismail Hadj Ahmed & Abdelghani Djebbari

  2. Inserm, LTSI, UMR 1099, University of Rennes, 35000, Rennes, France

    Ismail Hadj Ahmed, Amar Kachenoura & Lotfi Senhadji

Authors
  1. Ismail Hadj Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelghani Djebbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amar Kachenoura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lotfi Senhadji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelghani Djebbari.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hadj Ahmed, I., Djebbari, A., Kachenoura, A. et al. Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal. J Supercomput 78, 13680–13709 (2022). https://doi.org/10.1007/s11227-022-04387-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04387-6

Keywords

Search

Navigation