Skip to main content

Advertisement

SpringerLink
Log in

Cognitive IoT-Cloud Integration for Smart Healthcare: Case Study for Epileptic Seizure Detection and Monitoring

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

We propose a cognitive Internet of Things (IoT)–cloud-based smart healthcare framework, which communicates with smart devices, sensors, and other stakeholders in the healthcare environment; makes an intelligent decision based on a patient’s state; and provides timely, low-cost, and accessible healthcare services. As a case study, an EEG seizure detection method using deep learning is also proposed to access the feasibility of the cognitive IoT–cloud smart healthcare framework. In the proposed method, we use smart EEG sensors (apart from general healthcare smart sensors) to record and transmit EEG signals from epileptic patients. Thereafter, the cognitive framework makes a real-time decision on future activities and whether to send the data to the deep learning module. The proposed system uses the patient’s movements, gestures, and facial expressions to determine the patient’s state. Signal processing and seizure detection take place in the cloud, while signals are classified as seizure or non-seizure with a probability score. The results are transmitted to medical practitioners or other stakeholders who can monitor the patients and, in critical cases, make the appropriate decisions to help the patient. Experimental results show that the proposed model achieves an accuracy and sensitivity of 99.2 and 93.5%, respectively.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Yin Y, Zeng Y, Chen X, Fan Y (2016) The internet of things in healthcare: an overview. Journal of Industrial Information Integration 1:3–13

    Article  Google Scholar 

  2. Chen M, Zhang Y, Qiu M, Guizani N, Hao Y (2018) SPHA: smart personal health advisor based on deep analytics. IEEE Communications 56(3):164–169

    Article  Google Scholar 

  3. Hu, L et al (2016) Internet of Things Cloud: Architecture and Implementation. IEEE Commun Mag 54(12-Supp):32–39

  4. Chen, M et al (2018) Edge-CoCaCo: Toward Joint Optimization of Computation, Caching, and Communication on Edge Cloud. IEEE Wireless Commun 25(3):21–27

    Article  Google Scholar 

  5. Muhammad G, Rahman SKMM, Alelaiwi A, Alamri A (2017) Smart health solution integrating IoT and cloud: a case study of voice pathology monitoring. IEEE Commun Mag 55(1):69–73

    Article  Google Scholar 

  6. Chen M, Herrera F, Hwang K (2018) Cognitive computing: architecture, technologies and intelligent applications. IEEE Access 6:19774–19783

    Article  Google Scholar 

  7. Hossain, MS (2016) Patient State Recognition System for Healthcare Using Speech and Facial Expressions. J Medical Systems 40(12):272:1–272:8  

    Article  Google Scholar 

  8. Hossain MS, Muhammad G (2016) Audio-visual emotion recognition using multi-directional regression and Ridgelet transform. Journal on Multimodal User Interfaces 10(4):325–333

    Article  Google Scholar 

  9. Hossain MS et al (2015) Audio–visual emotion-aware cloud gaming framework. IEEE Transactions on Circuits and Systems for Video Technology 25(12):2105–2118

    Article  Google Scholar 

  10. Zanella A (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32

    Article  Google Scholar 

  11. Hossain MS, Muhammad G (2016) Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  12. Chen M, Li W, Hao Y, Qian Y, Humar I (2018) Edge cognitive computing based smart healthcare system. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.03.054

    Article  Google Scholar 

  13. World Health Organization (2017) Available: http://www.who.int/mediacentre/factsheets/fs999/en/

  14. Litt B, Echauz J (2002) Prediction of epileptic seizures. Lancet Neurol 1:22–30. https://doi.org/10.1016/S1474-4422(02)00003-0

    Article  Google Scholar 

  15. Chen M, Yang J, Hao Y, Mao S, Hwang K (2017) A 5G cognitive system for healthcare. Big Data Cogn Comput 1(1):2

    Article  Google Scholar 

  16. Qian Y, Chen M et al (2018) Secure enforcement in cognitive internet of vehicles. IEEE Internet of Things Journal 5(2):1242–1250

    Article  Google Scholar 

  17. Hossain MS, Muhammad G, Al-Qurishi M (2018) Verifying the images authenticity in cognitive internet of things (CIoT)-oriented cyber physical system. MONET 23(2):239–250

    Google Scholar 

  18. Zhang M et al (2012) Cognitive internet of things: concepts and application example. Int J Comput Sci Issues 9(3):151–158

    Google Scholar 

  19. Hossain MS, Muhammad G (2017) Emotion-aware connected healthcare big data towards 5G. IEEE Internet of Things Journal 5(4):2399-2406 (2018)

    Article  Google Scholar 

  20. Hossain MS, Muhammad G, Alamri A (2017) Smart healthcare monitoring: a voice pathology detection paradigm for smart cities. Multimedia Systems. https://doi.org/10.1007/s00530-017-0561-x

  21. Samani H, Zhu R (2016) Robotic automated external defibrillator ambulance for emergency medical service in smart cities. IEEE Access 4:268–283

    Article  Google Scholar 

  22. Muhammad G, Alsulaiman M, Amin SU, Ghoneim A, Alhamid MF (2017) A facial-expression monitoring system for improved healthcare in smart cities. IEEE Access 5(1):10871–10881

    Article  Google Scholar 

  23. Chen M, Song J, Lai C, Hu B (2016) Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mob Netw Appl 21(5):825–845

    Article  Google Scholar 

  24. Vlacheas P et al (2013) Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun Mag 51(6):102–111

    Article  Google Scholar 

  25. Piniewski B et al (2010) Empowering healthcare patients with smart technology. Computer 43(7):27–34

    Article  Google Scholar 

  26. Ghoneim A, Muhammad G, Amin SU, Gupta B (2018) Medical image forgery detection for smart healthcare. IEEE Commun Mag 56(4):33–37

    Article  Google Scholar 

  27. Muhammad G (2015) Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Cluster Computing (Springer) 18(2):795–802

    Article  MathSciNet  Google Scholar 

  28. Hills M (2014). Seizure detection using FFT, Temporal and Spectral Correlation Coefficients, Eigenvalues and Random Forest. Technical Report. Github

  29. Park Y, Luo L, Parhi KK, Netoff T (2011) Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52:1761–1770. https://doi.org/10.1111/j.1528-1167.2011.03138.x

    Article  Google Scholar 

  30. Parvez MZ, Paul M (2015) Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals. IET Signal Processing 9(6):467–475. https://doi.org/10.1049/iet-spr.2013.0288

    Article  Google Scholar 

  31. Thodoroff P, Pineau J, Lim A (2016). Learning robust features using deep learning for automatic seizure detection, Proceedings of the 1st Machine Learning for Healthcare Conference. PMLR 56:178–190

  32. Panayiotopoulos, CP (2010) A Clinical Guide To Epileptic Syndromes And Their Treatment. Springer-Verlag, London, pp 654

    Book  Google Scholar 

  33. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (ed) Advances in neural information processing systems 25, pages 1097–1105. Curran Associates, Inc.. URL http://papers.nips.cc/paper/ 4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

  34. Pramod S, Page A, Mohsenin T, Oates T (2014) Detecting epileptic seizures from EEG data using neural networks. arXiv preprint arXiv 1412:6502

    Google Scholar 

  35. Turner JT, Page A, Mohsenin T, Oates T (2014) Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. In 2014 AAAI Spring Symposium Series

  36. Li D, Wang G, Song T, Jin Q (2016) Improving convolutional neural network using accelerated proximal gradient method for an epilepsy diagnosis. In Control (CONTROL), 2016 UKACC 11th International Conference on. IEEE, 1–6

  37. Shoeb A (2009) Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. Ph.D. thesis, Massachusetts Institute of Technology

  38. Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, Knight RT (2006) High gamma power is phase-locked to theta oscillations in human neocortex. Science 313:1626–1628

    Article  Google Scholar 

  39. Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38:5391–5420. https://doi.org/10.1002/hbm.23730

    Article  Google Scholar 

  40. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153

    Google Scholar 

  41. Wilson S, Scheuer M, Emerson R, Gabor A (2004) Seizure detection: evaluation of the Reveal algorithm. Clin Neurophysiol 10:2280–2291

    Article  Google Scholar 

  42. Fergus P, Hussain A, Hignett D, Al-Jumeily D, Abdel-Aziz K, Hamdan H (2016) A machine learning system for automated whole-brain seizure detection. Applied Computing and Informatics 12(1):70–89, ISSN 2210-8327. https://doi.org/10.1016/j.aci.2015.01.001

    Article  Google Scholar 

  43. Supartak A, Li L, Guo Y (2014) Feature extraction with stacked autoencoders for epileptic seizure detection, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, pp. 4184–4187. https://doi.org/10.1109/EMBC.2014.6944546

  44. Xun G, Jia X, Zhang A (2015) Context-learning based electroencephalogram analysis for epileptic seizure detection, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, 2015, pp. 325–330. https://doi.org/10.1109/BIBM.2015.7359702

  45. Chen D, Wan S, Xiang J, Bao FS (2017) A high-performance seizure detection algorithm based on discrete wavelet transform (DWT) and EEG. PLoS One 12(3):e0173138. https://doi.org/10.1371/journal.pone.0173138

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia through the Research Group Project No: RG-1436-016

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Musaed Alhussein, Ghulam Muhammad & Syed Umar Amin

  2. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    M. Shamim Hossain

Authors
  1. Musaed Alhussein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghulam Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Shamim Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Syed Umar Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Shamim Hossain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alhussein, M., Muhammad, G., Hossain, M.S. et al. Cognitive IoT-Cloud Integration for Smart Healthcare: Case Study for Epileptic Seizure Detection and Monitoring. Mobile Netw Appl 23, 1624–1635 (2018). https://doi.org/10.1007/s11036-018-1113-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1113-0

Keywords

Search

Navigation