Advertisement

Cerebral Blood Flow Monitoring Using IoT Enabled Cloud Computing for mHealth Applications

  • Beulah Preethi Vallur
  • Krishna Murthy Kattiyan Ramamoorthy
  • Shahnam Mirzaei
  • Shahram Mirzai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

This paper presents a novel application of cloud computing enabled by Internet of Things (IoT) in monitoring parameters affecting cerebral blood flow (CBF) which is the movement of blood through the network of cerebral arteries and veins supplying the brain. The example design implemented in this proposal can be easily replaced with similar applications to generalize the concept offered by our work. This enables healthcare professionals have ubiquitous access to the processed medical data (in this case cerebral blood flow) of patients during their treatment process. Using cloud computing, accessing data from multiple locations has become easier. Big Data analytic frameworks enabled by IoT and cloud computing methods present new opportunities to extract new knowledge and create novel applications in health care domain. This paper shows one of the novel methods to improve the quality of health care data processing inside the cloud. Our scheme proposes a design in which the cerebral circulation data is captured using sensors connected to Raspberry Pi and then pushed to the cloud, stored in database, processed, and analyzed. Results then will be retrieved and distributed to medical professionals via Android mobile application. This application is designed to keep track of cerebral circulation and process the data obtained through the sensors. Anomalies, such as oxygen imbalance, internal bleeding, swelling due to an increase of water, and disturbance in blood flow that can lead to serious health issues can be detected. We have used Amazon web services (AWS) cloud platform to perform cloud services. Our approach is inspired by Amazon Simple Beer Service (SBS) [10]; a cloud-connected kegerator; that sends sensor data (beer flow and in our case cerebral circulation data flow) to AWS [5]. SBS publishes sensor data collected by an IoT enabled device (Raspberry Pi) to an AWS application program interface (API) gateway over Hypertext Transfer Protocol Secure (HTTPS). To the best of our knowledge this is the first scheme offered to replace the manual process of monitoring CBF using biomedical electronic devices.

Keywords

IoT Cloud computing Cerebral blood flow (CBF) mHealth Cerebral perfusion pressure (CPP) 

References

  1. 1.
    Impacts of Cloud Computing on Healthcare, Version 2.0 (2017 February). http://www.cloud-council.org
  2. 2.
    Hill, L., Gwinnutt, C.: Cerebral blood flow and intracranial pressure. Anaesthesia 6, 153 (2005)Google Scholar
  3. 3.
    Kirby, B.J.: Micro and Nanoscale Fluid Mechanics: Transport in Microfluidic Devices. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  4. 4.
    Kirkman, M.A., Smith, M.: Intracranial pressure monitoring, cerebral perfusion pressure estimation, and ICP/CPP-guided therapy. Br. J. Anaesth. 112, 35 (2014)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Alali, A.S., et al.: Intracranial pressure monitoring among children with severe traumatic brain injury. J. Neurosurg. Pediatr. 16, 523 (2015)CrossRefGoogle Scholar
  7. 7.
    Dawes, A.J., et al.: Intracranial pressure monitoring and inpatient mortality in severe traumatic brain injury: a prosperity score matched analysis. J. Trauma Acute Care Surg. 78, 492 (2015)CrossRefGoogle Scholar
  8. 8.
    Marcus, H.J., Wilson, M.H.: Insertion of an intracranial-pressure monitor. N. Engl. J. Med. 26(373), e25 (2015)CrossRefGoogle Scholar
  9. 9.
    Kang, S.K., et al.: Bioresorbable silicon electronic sensors for the brain. Nature 530, 71 (2016)CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.
    Donnelly, J., et al.: Regulation of the cerebral circulation: bedside assessment and clinical implications. J. Crit. Care 20, 129 (2016)CrossRefGoogle Scholar
  13. 13.
    Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Commun. Sci. 33, 163 (2007)CrossRefGoogle Scholar
  14. 14.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Beulah Preethi Vallur
    • 1
  • Krishna Murthy Kattiyan Ramamoorthy
    • 1
  • Shahnam Mirzaei
    • 1
  • Shahram Mirzai
    • 2
  1. 1.Department of Electrical and Computer EngineeringCalifornia State University NorthridgeNorthridgeUSA
  2. 2.Neurosurgery DivisionBamberg HospitalBambergGermany

Personalised recommendations