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An Overview of Health Monitoring Systems for Arrhythmia Patients

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Modelling and Implementation of Complex Systems (MISC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 593))

Abstract

Cardiac rhythm disorders (arrhythmias) are one of the leading causes of death worldwide. Therefore, the detection and classification of arrhythmias are essential for diagnosing patients with cardiac abnormalities. With new technologies, we can see the opening of medical institutions toward health information technology systems. To give researchers an overview of existing works about health monitoring systems for heart patients, we have established a comparative study between recent and well-known methods based on their results. The focus is in this comparison on the features used, the signal length, the datasets used, features extraction methods, features selection methods, classification methods, and the performances of each method. Furthermore, we classified these works by disease types (Paroxysmal Atrial Fibrillation PAF, Atrial Fibrillation AF, Ventricular Tachyarrhythmia VTA such as Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), Sudden Cardiac Death SCD, Obstructive Sleep Apnea OSA).

According to this comparative study, it has been found that many studies got exciting results. However, the classification rate achieved remains moderate. Moreover, heart rate monitoring devices are not within reach of the average citizen in terms of price and prediction time, and more studies are needed using more extensive databases. This study gives a comprehensive view of what is currently being done to monitor heart patients’ health. After discussing the achievements and limitations of existing approaches to monitoring the status of cardiac patients, we conclude by providing several potential research directions for the future.

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References

  1. Al-Angari, H.M., Sahakian, A.V.: Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier. IEEE Trans. Inf Technol. Biomed. 16(3), 463–468 (2012)

    Article  Google Scholar 

  2. Atri, R., Mohebbi, M.: Obstructive sleep apnea detection using spectrum and bispectrum analysis of single-lead ECG signal. Physiol. Meas. 36(9), 1963 (2015)

    Article  Google Scholar 

  3. Baim, D.S., et al.: Survival of patients with severe congestive heart failure treated with oral milrinone. J. Am. Coll. Cardiol. 7(3), 661–670 (1986). https://doi.org/10.1016/S0735-1097(86)80478-8

    Article  Google Scholar 

  4. Clifford, G., et al.: AF classification from a short single lead ECG recording - the PhysioNet computing in cardiology challenge 2017 (2017). https://physionet.org/content/challenge-2017/1.0.0/

  5. Ebrahimzadeh, E., et al.: An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal. Comput. Methods Programs Biomed. 169, 19–36 (2019)

    Article  Google Scholar 

  6. Ebrahimzadeh, E., Kalantari, M., Joulani, M., Shahraki, R.S., Fayaz, F., Ahmadi, F.: Prediction of paroxysmal atrial fibrillation: a machine learning based approach using combined feature vector and mixture of expert classification on HRV signal. Comput. Methods Programs Biomed. 165, 53–67 (2018)

    Article  Google Scholar 

  7. Ebrahimzadeh, E., Manuchehri, M.S., Amoozegar, S., Araabi, B.N., Soltanian-Zadeh, H.: A time local subset feature selection for prediction of sudden cardiac death from ECG signal. Med. Biol. Eng. Comput. 56(7), 1253–1270 (2018)

    Article  Google Scholar 

  8. Evrengul, H., et al.: The relationship between heart rate recovery and heart rate variability in coronary artery disease. Ann. Noninvasive Electrocardiol. 11(2), 154–162 (2006)

    Article  Google Scholar 

  9. Nolle, F.M., Badura, F.K., Catlett, J.M., Bowser, R.W., Sketch, M.H.: CREI-GARD, a new concept in computerized arrhythmia monitoring systems (1986)

    Google Scholar 

  10. Moody, G., Goldberger, A., McClennen, S., Swiryn, S.: Predicting the onset of paroxysmal atrial fibrillation: the computers in cardiology challenge 2001 (2001). https://physionet.org/content/afpdb/1.0.0/

  11. Goldberger, A., et al.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Google Scholar 

  12. Goldberger, A., et al.: Spontaneous ventricular tachyarrhythmia database (2007). https://physionet.org/content/mvtdb/1.0/

  13. Gutiérrez-Tobal, G.C., Álvarez, D., Del Campo, F., Hornero, R.: Utility of AdaBoost to detect sleep apnea-hypopnea syndrome from single-channel airflow. IEEE Trans. Biomed. Eng. 63(3), 636–646 (2015)

    Article  Google Scholar 

  14. Hwang, S.H., Lee, Y.J., Jeong, D.U., Park, K.S., et al.: Apnea-hypopnea index prediction using electrocardiogram acquired during the sleep-onset period. IEEE Trans. Biomed. Eng. 64(2), 295–301 (2016)

    Google Scholar 

  15. Inserm: Apnèe du sommeil. une source de fatigue, mais aussi de maladies cardiovasculaires. Website page (2017). https://www.inserm.fr/dossier/apnee-sommeil

  16. Jager, F., et al.: Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med. Biol. Eng. Comput. 41(2), 172–183 (2003)

    Article  Google Scholar 

  17. Khandoker, A.H., Gubbi, J., Palaniswami, M.: Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings. IEEE Trans. Inf Technol. Biomed. 13(6), 1057–1067 (2009)

    Article  Google Scholar 

  18. Krasteva, V., Jekova, I.: Assessment of ECG frequency and morphology parameters for automatic classification of life-threatening cardiac arrhythmias. Physiol. Meas. 26(5), 707 (2005)

    Article  Google Scholar 

  19. Liew, R.: Electrocardiogram-based predictors of sudden cardiac death in patients with coronary artery disease. Clin. Cardiol. 34(8), 466–473 (2011)

    Article  Google Scholar 

  20. Mandal, S., Mondal, P., Roy, A.H.: Detection of ventricular arrhythmia by using heart rate variability signal and ECG beat image. Biomed. Signal Process. Control 68, 102692 (2021)

    Google Scholar 

  21. Marinucci, D., Sbrollini, A., Marcantoni, I., Morettini, M., Swenne, C.A., Burattini, L.: Artificial neural network for atrial fibrillation identification in portable devices. Sensors 20(12), 3570 (2020)

    Article  Google Scholar 

  22. Moody, G., Mark, R.: A new method for detecting atrial fibrillation using R-R intervals. Comput. Cardiol. 10, 227–230 (1983)

    Google Scholar 

  23. Iyengar, N., Peng, C.K., Morin, R., Goldberger, A.L., Lipsitz, L.A.: Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am. J. Physiol. 271, 1078–1084 (1996)

    Google Scholar 

  24. Nguyen, H.D., Wilkins, B.A., Cheng, Q., Benjamin, B.A.: An online sleep apnea detection method based on recurrence quantification analysis. IEEE J. Biomed. Health Inform. 18(4), 1285–1293 (2013)

    Article  Google Scholar 

  25. World Health Organization: L’oms lève le voile sur les principales causes de mortalité et d’incapacité dans le monde: 2000–2019. Website page (2020). https://www.who.int/fr/news/item/09-12-2020-who-reveals-leading -causes-of-death-and-disability-worldwide-2000-2019

  26. Parsi, A., Byrne, D., Glavin, M., Jones, E.: Heart rate variability feature selection method for automated prediction of sudden cardiac death. Biomed. Signal Process. Control 65, 102310 (2021)

    Google Scholar 

  27. Parsi, A., Glavin, M., Jones, E., Byrne, D.: Prediction of paroxysmal atrial fibrillation using new heart rate variability features. Comput. Biol. Med. 133, 104367 (2021)

    Google Scholar 

  28. Penzel, T., Moody, G., Mark, R., Goldberger, A., Peter, J.: The apnea-ECG database. In: Computers in Cardiology 2000 (Cat. 00CH37163), vol. 27, pp. 255–258. IEEE (2000). https://doi.org/10.1109/CIC.2000.898505

  29. Rohila, A., Sharma, A.: Detection of sudden cardiac death by a comparative study of heart rate variability in normal and abnormal heart conditions. Biocybern. Biomed. Eng. 40(3), 1140–1154 (2020)

    Article  Google Scholar 

  30. Sbrollini, A., et al.: Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach. Biomed. Eng. Online 18(1), 1–17 (2019)

    Article  Google Scholar 

  31. Schuch, S., Tipper, S.P.: On observing another person’s actions: influences of observed inhibition and errors. Percept. Psychophys. 69(5), 828–837 (2007)

    Article  Google Scholar 

  32. Greenwald, S.D.: Development and analysis of a ventricular fibrillation detector. Master’s thesis, MIT Dept. of Electrical Engineering and Computer Science, Cambridge (1986)

    Google Scholar 

  33. Shen, M., Zhang, L., Luo, X., Xu, J.: Atrial fibrillation detection algorithm based on manual extraction features and automatic extraction features. In: IOP Conference Series: Earth and Environmental Science. vol. 428, p. 012050. IOP Publishing (2020)

    Google Scholar 

  34. Smruthy, A., Suchetha, M.: Real-time classification of healthy and apnea subjects using ECG signals with variational mode decomposition. IEEE Sens. J. 17(10), 3092–3099 (2017)

    Article  Google Scholar 

  35. Taye, G.T., Hwang, H.J., Lim, K.M.: Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features. Sci. Rep. 10(1), 1–7 (2020)

    Google Scholar 

  36. Taye, G.T., Shim, E.B., Hwang, H.J., Lim, K.M.: Machine learning approach to predict ventricular fibrillation based on QRS complex shape. Front. Physiol. 10, 1193 (2019)

    Article  Google Scholar 

  37. **e, B., Minn, H.: Real-time sleep apnea detection by classifier combination. IEEE Trans. Inf Technol. Biomed. 16(3), 469–477 (2012)

    Article  Google Scholar 

  38. Zarei, A., Asl, B.M.: Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals. Biomed. Signal Process. Control 59, 101927 (2020)

    Google Scholar 

  39. Zarei, A., Asl, B.M.: Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal. Comput. Methods Programs Biomed. 195, 105626 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LaSTIC Laboratory, Department of Computer Science, University of Batna 2, Batna, Algeria

    Saoueb Kerdoudi, Larbi Guezouli & Tahar Dilekh

  2. Higher National School of Renewable Energies, Environment & Sustainable Development, Batna, Algeria

    Larbi Guezouli

Authors
  1. Saoueb Kerdoudi
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  2. Larbi Guezouli
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  3. Tahar Dilekh
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Corresponding author

Correspondence to Saoueb Kerdoudi .

Editor information

Editors and Affiliations

  1. Faculty of New Information and Communication Technologies, University of Constantine2, Constantine, Algeria

    Salim Chikhi

  2. Sistemas Informáticos, Universidad de Castilla - La Mancha, Albacete, Spain

    Gregorio Diaz-Descalzo

  3. Faculty of Technology, University of Saida, Saida, Algeria

    Abdelmalek Amine

  4. Faculty of Information and Communication Technology, University of Constantine2, Constantine, Algeria

    Allaoua Chaoui

  5. Faculty of New Information and Communication Technologies, Université Constantine 2, Constantine, Algeria

    Djamel Eddine Saidouni

  6. Department of Mathematics and Computer Science, University of El Oued, El-Oued, Algeria

    Mohamed Khireddine Kholladi

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Kerdoudi, S., Guezouli, L., Dilekh, T. (2023). An Overview of Health Monitoring Systems for Arrhythmia Patients. In: Chikhi, S., Diaz-Descalzo, G., Amine, A., Chaoui, A., Saidouni, D.E., Kholladi, M.K. (eds) Modelling and Implementation of Complex Systems. MISC 2022. Lecture Notes in Networks and Systems, vol 593. Springer, Cham. https://doi.org/10.1007/978-3-031-18516-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-18516-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18515-1

  • Online ISBN: 978-3-031-18516-8

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