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Integration of Internet of Things and Cloud Computing for Cardiac Health Recognition

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Metaheuristics in Machine Learning: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 967))

  • The original version of this chapter was revise. The author M. Hassaballah’s affiliation has been updated with new affiliation. The correction to this chapter can be found at https://doi.org/10.1007/978-3-030-70542-8_31

Abstract

The Internet of Things (IoT) plays a very important role in various healthcare applications. The advancement of IoT and cloud computing facilitates the patient’s health, employee retention, and organizational quality in the medical sector. The study analyses the new IoT materials, implementations, and industry patterns for healthcare services. We also consider the way in which promising technologies such as cloud computing, immersive care home, Big data, and wearable sensors. Furthermore, this study analyses protection and safety, authentication, energy, control, maintenance, service quality, and real-time wireless health monitoring which are very problematic in many IoT healthcare architectures. Due to the lack of well-established system architecture, data constraint and the preservation of its privacy remain a challenge. The main aim of this survey is to analyze the purpose of healthcare based on the digital healthcare system. It also reports on a range of IoT and e-health policies and structures that determine whether to ease any sustainable growth.

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Change history

  • 11 October 2021

    The original version of the book was published with incorrect affiliation for the author M. Hassaballah. Affiliation has been updated with correct affiliation for the following chapters:

References

  1. L. Atzori, A. Iera, G. Morabito, The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)

    Article  MATH  Google Scholar 

  2. A.S. Arefin, K.T. Nahiyan, M. Rabbani, The basics of healthcare IoT: data acquisition, medical devices, instrumentations and measurements, in A Handbook of Internet of Things in Biomedical and Cyber Physical System (Springer, 2020), pp. 1–37

    Google Scholar 

  3. E.H. Houssein, M.R. Saad, K. Hussain, W. Zhu, H. Shaban, M. Hassaballah, Optimal sink node placement in large scale wireless sensor networks based on Harris’ hawk optimization algorithm. IEEE Access 8, 19381–19397 (2020)

    Article  Google Scholar 

  4. M.M. Ahmed, E.H. Houssein, A.E. Hassanien, A. Taha, E. Hassanien, Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun. Syst. 72(2), 243–259 (2019)

    Article  Google Scholar 

  5. T. Wu, F. Wu, J.-M. Redoute, M.R. Yuce, An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5, 11413–11422 (2017)

    Article  Google Scholar 

  6. S. Chen, N. Wu, S. Lin, J. Duan, Z. Xu, Y. Pan, H. Zhang, Z. Xu, L. Huang, B. Hu et al., Hierarchical elastomer tuned self-powered pressure sensor for wearable multifunctional cardiovascular electronics. Nano Energy 70, 104460 (2020)

    Article  Google Scholar 

  7. M. Foster, R. Brugarolas, K. Walker, S. Mealin, Z. Cleghern, S. Yuschak, J. Condit, D. Adin, J. Russenberger, M. Gruen et al., Preliminary evaluation of a wearable sensor system for heart rate assessment in guide dog puppies. IEEE Sens. J. (2020)

    Google Scholar 

  8. L. Al-Ghussain, S. El Bouri, H. Liu, D. Zheng et al., Clinical evaluation of stretchable and wearable inkjet-printed strain gauge sensor for respiratory rate monitoring at different measurements locations. J. Clin. Monit. Comput. 1–10 (2020)

    Google Scholar 

  9. H. Legenvre, M. Henke, H. Ruile, Making sense of the impact of the internet of things on purchasing and supply management: a tension perspective. J. Purchas. Supply Manag. 26(1), 100596 (2020)

    Article  Google Scholar 

  10. S. Selvaraj, S. Sundaravaradhan, Challenges and opportunities in IoT healthcare systems: a systematic review. SN Appl. Sci. 2(1), 139 (2020)

    Article  Google Scholar 

  11. A. Karim, A. Siddiqa, Z. Safdar, M. Razzaq, S.A. Gillani, H. Tahir, S. Kiran, E. Ahmed, M. Imran, Big data management in participatory sensing: issues, trends and future directions. Future Gener. Comput. Syst. 107, 942–955 (2020)

    Article  Google Scholar 

  12. P. O’Donovan, C. Gallagher, K. Leahy, D.T. O’Sullivan, A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications. Comput. Ind. 110, 12–35 (2019)

    Google Scholar 

  13. A. Baouya, S. Chehida, S. Bensalem, M. Bozga, Fog computing and blockchain for massive IoT deployment, in 2020 9th Mediterranean Conference on Embedded Computing (MECO) (IEEE, 2020), pp. 1–4

    Google Scholar 

  14. H. Djelouat, M. Al Disi, I. Boukhenoufa, A. Amira, F. Bensaali, C. Kotronis, E. Politi, M. Nikolaidou, G. Dimitrakopoulos, Real-time ecg monitoring using compressive sensing on a heterogeneous multicore edge-device. Microprocessors Microsystems 72, 102839 (2020)

    Google Scholar 

  15. M. Kilany, E.H. Houssein, A.E. Hassanien, A. Badr, Hybrid water wave optimization and support vector machine to improve EMG signal classification for neurogenic disorders, in 2017 12th International Conference on Computer Engineering and Systems (ICCES) (IEEE, 2017), pp. 686–691

    Google Scholar 

  16. E.H. Houssein, A.E. Hassanien, A.A. Ismaeel, EEG signals classification for epileptic detection: a review, in Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing (2017), pp. 1–9

    Google Scholar 

  17. A. Hamad, E.H. Houssein, A.E. Hassanien, A.A. Fahmy, A hybrid eeg signals classification approach based on grey wolf optimizer enhanced SVMs for epileptic detection, in International Conference on Advanced Intelligent Systems and Informatics (Springer, 2017), pp. 108–117

    Google Scholar 

  18. S. Liu, Y. Li, X. Zeng, H. Wang, P. Yin, L. Wang, Y. Liu, J. Liu, J. Qi, S. Ran et al., Burden of cardiovascular diseases in china, 1990–2016: findings from the 2016 global burden of disease study. JAMA Cardiol. 4(4), 342–352 (2019)

    Article  Google Scholar 

  19. M. Batra, J. Linsky, R. Heydon, B. Redding, L.G. Richardson, Bluetooth connectionless slave broadcast burst mode, US Patent App. 16/250,837, 19 March 2020

    Google Scholar 

  20. S.R. Islam, D. Kwak, M.H. Kabir, M. Hossain, K.-S. Kwak, The internet of things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2015)

    Article  Google Scholar 

  21. A.A. Mutlag, M.K. Abd Ghani, N.A. Arunkumar, M.A. Mohammed, O. Mohd, Enabling technologies for fog computing in healthcare IoT systems. Future Gener. Comput. Syst. 90, 62–78 (2019)

    Google Scholar 

  22. A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, Fog computing for healthcare 4.0 environment: opportunities and challenges. Comput. Electr. Eng. 72, 1–13 (2018)

    Google Scholar 

  23. M. García-Valls, C. Calva-Urrego, A. García-Fornes, Accelerating smart ehealth services execution at the fog computing infrastructure. Future Gener. Comput. Syst. 108, 882–893 (2020)

    Article  Google Scholar 

  24. B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant, K. Mankodiya, Towards fog-driven IoT ehealth: promises and challenges of IoT in medicine and healthcare. Future Gener. Comput. Syst. 78, 659–676 (2018)

    Article  Google Scholar 

  25. H. Ahmadi, G. Arji, L. Shahmoradi, R. Safdari, M. Nilashi, M. Alizadeh, The application of internet of things in healthcare: a systematic literature review and classification. Univ. Access Inf. Soc. 18, 837–869 (2019)

    Article  Google Scholar 

  26. S.B. Baker, W. Xiang, I. Atkinson, Internet of things for smart healthcare: technologies, challenges, and opportunities. IEEE Access 5, 26521–26544 (2017)

    Article  Google Scholar 

  27. F.A. Kraemer, A.E. Braten, N. Tamkittikhun, D. Palma, Fog computing in healthcare—a review and discussion. IEEE Access 5, 9206–9222 (2017)

    Article  Google Scholar 

  28. J. Qian, P. Tiwari, S.P. Gochhayat, H.M. Pandey, A noble double dictionary based ECG compression technique for IoTH. IEEE Internet Things J. (2020)

    Google Scholar 

  29. D. Hasan, A. Ismaeel, Designing ECG monitoring healthcare system based on internet of things Blynk application. J. Appl. Sci. Technol. Trends 1(3), 106–111 (2020)

    Article  Google Scholar 

  30. M.A. Serhani, H.T. El Kassabi, H. Ismail, A. Nujum Navaz, ECG monitoring systems: review, architecture, processes, and key challenges. Sensors 20(6), 1796 (2020)

    Google Scholar 

  31. G. Ramesh, N.M. Kumar, Design of RZF antenna for ECG monitoring using IoT. Multimed. Tools Appl. 79(5), 4011–4026 (2020)

    Article  Google Scholar 

  32. T. Tekeste, H. Saleh, B. Mohammad, M. Ismail, Ultra-low power QRS detection and ECG compression architecture for IoT healthcare devices. IEEE Trans. Circ. Syst. I: Reg. Pap. 66(2), 669–679 (2018)

    Google Scholar 

  33. A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Article  Google Scholar 

  34. A. Singhal, P. Singh, B. Fatimah, R.B. Pachori, An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomed. Signal Process. Control 57, 101741 (2020)

    Article  Google Scholar 

  35. I. Houamed, L. Saidi, F. Srairi, ECG signal denoising by fractional wavelet transform thresholding. Res. Biomed. Eng. 36(3), 349–360 (2020)

    Article  Google Scholar 

  36. A. Chen, Y. Zhang, M. Zhang, W. Liu, S. Chang, H. Wang, J. He, Q. Huang, A real time QRS detection algorithm based on ET and PD controlled threshold strategy. Sensors 20(14), 4003 (2020)

    Article  Google Scholar 

  37. A.K. Dohare, V. Kumar, R. Kumar, An efficient new method for the detection of QRS in electrocardiogram. Comput. Electr. Eng. 40(5), 1717–1730 (2014)

    Article  Google Scholar 

  38. A. Giorgio, C. Guaragnella, D.A. Giliberti, Improving ECG signal denoising using wavelet transform for the prediction of malignant arrhythmias. Int. J. Med. Eng. Inform. 12(2), 135–150 (2020)

    Google Scholar 

  39. C.K. Jha, M.H. Kolekar, Empirical mode decomposition and wavelet transform based ECG data compression scheme. IRBM (2020)

    Google Scholar 

  40. D. Lee, H. Kwon, H. Lee, C. Seo, K. Park, Optimal lead position in patch-type monitoring sensors for reconstructing 12-lead ECG signals with universal transformation coefficient. Sensors 20(4), 963 (2020)

    Article  Google Scholar 

  41. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)

    Article  Google Scholar 

  42. Y.-C. Yeh, W.-J. Wang, QRS complexes detection for ECG signal: the difference operation method. Comput. Methods Prog. Biomed. 91(3), 245–254 (2008)

    Article  Google Scholar 

  43. H. Li, X. Wang, L. Chen, E. Li, Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circ. Syst. Signal Process. 33(4), 1261–1276 (2014)

    Article  Google Scholar 

  44. S. Karpagachelvi, M. Arthanari, M. Sivakumar, ECG feature extraction techniques—a survey approach, arXiv preprint arXiv:1005.0957 (2010)

  45. A. Hamad, E.H. Houssein, A.E. Hassanien, A.A. Fahmy, Feature extraction of epilepsy EEG using discrete wavelet transform, in 201612th International Computer Engineering Conference (ICENCO) (IEEE, 2016), pp. 190–195

    Google Scholar 

  46. E.H. Houssein, M. Kilany, A.E. Hassanien, V. Snasel, A two-stage feature extraction approach for ecg signals, in International Afro-European Conference for Industrial Advancement (Springer, 2016), pp. 299–310

    Google Scholar 

  47. C.P. Igiri, Y. Singh, R.C. Poonia, A review study of modified swarm intelligence: particle swarm optimization, firefly, bat and gray wolf optimizer algorithms. Rec. Adv. Comput. Sci. Commun. (Formerly: Recent Patents Comput. Sci.) 13(1), 5–12 (2020)

    Google Scholar 

  48. E.H. Houssein, I.E. Mohamed, Y.M. Wazery, Salp swarm algorithm: a comprehensive review, in Applications of Hybrid Metaheuristic Algorithms for Image Processing (Springer, 2020), pp. 285–308

    Google Scholar 

  49. Association for the Advancement of Medical Instrumentation et al., Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms, ANSI/AAMI EC38, 1998

    Google Scholar 

  50. E.H. Houssein, M. Kilany, A.E. Hassanien, ECG signals classification: a review. Int. J. Intell. Eng. Inform. 5(4), 376–396 (2017)

    Google Scholar 

  51. E.H. Houssein, A.A. Ewees, M. Abd ElAziz, Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recogn. Image Anal. 28(2), 243–253 (2018)

    Google Scholar 

  52. Y. Kabalci, A survey on smart metering and smart grid communication. Renew. Sustain. Energy Rev. 57, 302–318 (2016)

    Article  Google Scholar 

  53. C. Gellweiler, Types of it architects: a content analysis on tasks and skills. J. Theor. Appl. Electron. Commerce Res. 15(2), 15–37 (2020)

    Article  Google Scholar 

  54. L.-F. Wang, J.-Q. Liu, B. Yang, X. Chen, C.-S. Yang, Fabrication and characterization of a dry electrode integrated gecko-inspired dry adhesive medical patch for long-term ECG measurement. Microsyst. Technol. 21(5), 1093–1100 (2015)

    Article  Google Scholar 

  55. M.M. Cvach, M. Biggs, K.J. Rothwell, C. Charles-Hudson, Daily electrode change and effect on cardiac monitor alarms: an evidence-based practice approach. J. Nurs. Care Qual. 28(3), 265–271 (2013)

    Article  Google Scholar 

  56. P. Verma, S.K. Sood, S. Kalra, Cloud-centric IoT based student healthcare monitoring framework. J. Ambient Intell. Hum. Comput. 9(5), 1293–1309 (2018)

    Article  Google Scholar 

  57. P. Pace, G. Aloi, R. Gravina, G. Fortino, G. Larini, M. Gulino, Towards interoperability of IoT-based health care platforms: the inter-health use case, in Proceedings of the 11th EAI International Conference on Body Area Networks (2016), pp. 12–18

    Google Scholar 

  58. A. Manashty, J. Light, U. Yadav, Healthcare event aggregation lab (heal), a knowledge sharing platform for anomaly detection and prediction, in 2015 17th International Conference on E-health Networking, Application & Services (HealthCom) (IEEE, 2015), pp. 648–652

    Google Scholar 

  59. X. Fafoutis, A. Elsts, R. Piechocki, I. Craddock, Experiences and lessons learned from making IoT sensing platforms for large-scale deployments. IEEE Access 6, 3140–3148 (2017)

    Article  Google Scholar 

  60. X. Fafoutis, A. Vafeas, B. Janko, R.S. Sherratt, J. Pope, A. Elsts, E. Mellios, G. Hilton, G. Oikonomou, R. Piechocki et al., Designing wearable sensing platforms for healthcare in a residential environment. EAI Endors. Trans. Pervasive Health Technol. 3(12) (2017)

    Google Scholar 

  61. M. Younan, E.H. Houssein, M. Elhoseny, A.A. Ali, Challenges and recommended technologies for the industrial internet of things: a comprehensive review. Measurement 151, 107198 (2020)

    Article  Google Scholar 

  62. M. Aljuaid, Q. Marashly, J. AlDanaf, I. Tawhari, M. Barakat, R. Barakat, B. Zobell, W. Cho, M.G. Chelu, N.F. Marrouche, Smartphone ECG monitoring system helps lower emergency room and clinic visits in post-atrial fibrillation ablation patients. Clin. Med. Insights: Cardiol. 14 (2020). https://doi.org/10.1177/1179546820901508

  63. S.-T. Hsieh, C.-L. Lin, Intelligent healthcare system using an Arduino microcontroller and an android-based smartphone. Biomed. Res. 28 (2017)

    Google Scholar 

  64. F. Sun, C. Yi, W. Li, Y. Li, A wearable H-shirt for exercise ECG monitoring and individual lactate threshold computing. Comput. Ind. 92, 1–11 (2017)

    Article  Google Scholar 

  65. V.P. Rachim, W.-Y. Chung, Wearable noncontact armband for mobile ECG monitoring system. IEEE Trans. Biomed. Circ. Syst. 10(6), 1112–1118 (2016)

    Article  Google Scholar 

  66. M. Mittal, C. Iwendi, A survey on energy-aware wireless sensor routing protocols. EAI Endors. Trans. Energy Web 6(24) (2019)

    Google Scholar 

  67. M. Mittal, L.K. Saraswat, C. Iwendi, J.H. Anajemba, A neuro-fuzzy approach for intrusion detection in energy efficient sensor routing, in 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) (IEEE, 2019), pp. 1–5

    Google Scholar 

  68. C. Iwendi, A. Allen, K. Offor, Smart security implementation for wireless sensor network nodes. J. Wirel. Sens. Netw. 1(1) (2015)

    Google Scholar 

  69. C. Iwendi, Z. Zhang, X. Du, ACO based key management routing mechanism for WSN security and data collection, in 2018 IEEE International Conference on Industrial Technology (ICIT) (IEEE, 2018), pp. 1935–1939

    Google Scholar 

  70. V. Mohindru, Y. Singh, R. Bhatt, Hybrid cryptography algorithm for securing wireless sensor networks from node clone attack. Recent Adv. Electr. Electron. Eng. (Formerly Recent Patents Electr. Electron. Eng.) 13(2), 251–259 (2020)

    Google Scholar 

  71. F.A. Hashim, E.H. Houssein, K. Hussain, M.S. Mabrouk, W. Al-Atabany, A modified Henry gas solubility optimization for solving motif discovery problem. Neural Comput. Appl. 32(14), 10759–10771 (2020)

    Article  Google Scholar 

  72. A.G. Hussien, A.E. Hassanien, E.H. Houssein, M. Amin, A.T. Azar, New binary whale optimization algorithm for discrete optimization problems. Engineering Optimization 52(6), 945–959 (2020)

    Article  MathSciNet  Google Scholar 

  73. E.H. Houssein, I.E. Mohamed, A.E. Hassanien, Salp swarm algorithm: tutorial. Swarm Intelligence Algorithms: A Tutorial (2020), p. 279

    Google Scholar 

  74. E. H. Houssein, Machine learning and meta-heuristic algorithms for renewable energy: a systematic review, in Advanced Control and Optimization Paradigms for Wind Energy Systems (Springer, 2019), pp. 165–187

    Google Scholar 

  75. E.H. Houssein, I.E. Mohamed, A.E. Hassanien, Salp swarm algorithm: modification and application, Swarm Intelligence Algorithms: Modifications and Applications (2020), p. 285

    Google Scholar 

  76. S. Preejith, R. Dhinesh, J. Joseph, M. Sivaprakasam, Wearable ECG platform for continuous cardiac monitoring, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2016), pp. 623–626

    Google Scholar 

  77. A.S. Ahouandjinou, K. Assogba, C. Motamed, Smart and pervasive ICU based-IoT for improving intensive health care, in 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART) (IEEE, 2016), pp. 1–4

    Google Scholar 

  78. A.M. Bianchi, M.O. Mendez, S. Cerutti, Processing of signals recorded through smart devices: sleep-quality assessment. IEEE Trans. Inf. Technol. Biomed. 14(3), 741–747 (2010)

    Article  Google Scholar 

  79. M.M. Baig, H. GholamHosseini, A.A. Moqeem, F. Mirza, M. Lindén, A systematic review of wearable patient monitoring systems—current challenges and opportunities for clinical adoption. J. Med. Syst. 41(7), 115 (2017)

    Article  Google Scholar 

  80. E. Jovanov, A. Milenkovic, Body area networks for ubiquitous healthcare applications: opportunities and challenges. J. Med. Syst. 35(5), 1245–1254 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein & Yaser M. Wazery

  2. Faculty of Computers and Information, Luxor University, Luxor, Egypt

    Ibrahim E. Ibrahim

  3. Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt

    M. Hassaballah

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  1. Essam H. Houssein
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  2. Ibrahim E. Ibrahim
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  3. M. Hassaballah
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  4. Yaser M. Wazery
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Correspondence to Essam H. Houssein .

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Editors and Affiliations

  1. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Diego Oliva

  2. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

  3. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Salvador Hinojosa

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Houssein, E.H., Ibrahim, I.E., Hassaballah, M., Wazery, Y.M. (2021). Integration of Internet of Things and Cloud Computing for Cardiac Health Recognition. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_26

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