A STUDY ON CLOUD BASED BIO-SIGNALS MANAGEMENT FRAMEWORK

Siddharth Arun Panigrahi; Umamaheswari V

doi:10.22159/ajpcr.2017.v10s1.19557

Authors

Siddharth Arun Panigrahi School of Electronics Engineering VIT Chennai Campus Chennai, India
Umamaheswari V School of Electronics Engineering VIT Chennai Campus Chennai, In

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19557

Keywords:

biosignals, statistical analysis, data management

Abstract

An analytical study of the complete framework for the management of biosignals is done. The framework provides for the acquisition, and storage of the biosignals, along with the associated metadata. It also provides solutions for validation, synchronization of acquired signals, thus allowing error-free signal inputs for further statistical analysis. The model comprises primarily of four layers, namely, acquisition, validation, post-processing and statistical analysis layers. Additionally, a presentation layer is also provided, wherein the appropriate end-user can use a suitable client or Web service to access the results of the statistical analysis. The raw data is deliberately spilt into two: Internal data (actual signal data) and External data (metadata) and they interact only when necessary (e.g. Identifying the biosignal's origin). Microservices are used to compartmentalize the functionalities required in the system. Additional solutions to problems plaguing the present models (like cloud-upload bottleneck) are also discussed.

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References

ThÃ¼rk F, Kampusch S, Kaniusas E. Management framework for biosignals in biomedical studies: From study design to data statistics. IEEE Trans Instrum Meas 2016;65(4):776-82.

Reilly S, Schallier W, Schrimpf S, Smit E, Wilkinson M. Report on Integration of Data and Publications. Opportunities Data Exchange, Technical Report; 2011.

Anderson NR, Lee ES, Brockenbrough JS, Minie ME, Fuller S, Brinkley J, et al. Issues in biomedical research data management and analysis: Needs and barriers. J Am Med Inform Assoc 2007;14(4):478-88.

Hey AJ, Trefethen AE. The data deluge: An e-science perspective. In: Grid Computing: Making the Global Infrastructure a Reality. New York, NY, USA: Wiley; 2003.

Kaniusas E. Biomedical Signals and Sensors I: Linking Physiological Phenomena and Biosignals. Berlin, Germany: Springer-Verlag; 2012.

Trigo JD, Alesanco A, MartÃnez I, GarcÃa J. A review on digital ECG formats and the relationships between them. IEEE Trans Inf Technol Biomed 2012;16(3):432-44.

ThÃ¼rk F, Kampusch S, Kaniusas E. Strategic framework for management of hybrid biosignals from study design to statistics. In: Proceeding Sensors Applications Symposium; 2015. p. 85-9.

Newman S. Building Microservices. Sebastopol, CA, USA: Oâ€™ReillyMedia, Inc.; 2015.

Zimmermann H. OSI reference model-the ISO model of architecture for open systems interconnection. IEEE Trans Commun 1980;28(4):425-32.

Pandey S, Voorsluys W, Niu S, Khandoker A, Buyya R. An autonomic cloud environment for hosting ECG data analysis services. Future Gener Comput Syst 2012;28(1):147-54.

Fielding RT. Architectural Styles and the Design of Network-based Software Architectures, Ph.D. Dissertation. Irvine, CA, USA: University California, Department of Information and Computer Science; 2000.

Rosenthal A, Mork P, Li MH, Stanford J, Koester D, Reynolds P. Cloud computing: A new business paradigm for biomedical information sharing. J Biomed Inform 2010;43(2):342-53.

Chen J, Qian F, Yan W, Shen B. Translational biomedical infor-matics in the cloud: Present and future. BioMed Res Int 2013;2013:8.

Moody GB, Mark RG, Goldberger AL. PhysioNet: A webbased resource for the study of physiologic signals. IEEE Eng Med Biol Mag 2001;20(3):70-5.