DOI : https://doi.org/10.18517/ijaseit.9.6.9804

Implementing a Web-based Application for Analysis and Evaluation of Heart Rate Variability Using Serverless Architecture

Mitko M. Gospodinov (1), Evgeniya Gospodinova (2)
(1) Scientific and Engineering Union, 5, Nish str. Veliko Tarnovo, 5000, Bulgaria
(2) Institute of Robotics, Akad. G. Bonchev str. block 2, Sofia, 1113, Bulgaria
Fulltext View | Download
How to cite (IJASEIT) :
Gospodinov, Mitko M., and Evgeniya Gospodinova. “Implementing a Web-Based Application for Analysis and Evaluation of Heart Rate Variability Using Serverless Architecture”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 1927-35, doi:10.18517/ijaseit.9.6.9804.
Citation Format :
This article is devoted to the development of a web-based application for analysis and evaluation of Heart Rate Variability (HRV) using serverless architecture. Advancements in information algorithms and computing technologies have been playing an increasingly important role in cardiology, as continuous monitoring of patients’ health can be vital to their well-being.  One physiological parameter that can be easily measured and that can provide indispensable insight into the state of the human body is the HRV.  HRV analysis can assess not only the physiological state of the body but also provide the capability to monitor its dynamics and predict future diseases. As the research in the sphere of cardiology is constantly growing there is a multitude of new ways to assess the physiological state of patients and provide an early indicator to pathological conditions. Therefore, there is a need to bring these advances to a growing number of end-users (health-care professionals and patients) in the shortest possible time. To address this problem, this study proposes the development of a web-based application for analysis and evaluation of HRV by applying linear and nonlinear mathematical methods. The application is created using a serverless architectural approach, which allows for fast development time, as there is no need to manage server infrastructure, and for automatic scaling to dynamically match the number of requests. The developer can instead focus on implementing the logic for the HRV analysis algorithms and deliver new improvements at a faster rate. The proposed web application can be accessed by any device that is connected to the Internet and is optimized to handle both an intermittent and a consistent volume of requests. The algorithms implemented in the web application have been validated by examining two groups of subjects (young adults and older adults) using linear and non-linear models. The obtained results from the two groups can be compared with a set of reference values (only for the linear methods) and an assessment can be made whether each studied parameter is within the normal range or outside it (its value is too high or too low). To aid the assessment for HRV, the results obtained by the linear and nonlinear analysis are presented using a set of both graphs and tables.

M. D. Costa, R. B. Davis, A. L. Goldberger, “Heart Rate Fragmentation: A New Approach to the Analysis or Cardiac Interbeat Internal Dynamics”, Frontiers in Psychology, 8:255, 2017.

U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim,and J. S. Suri, “Heart rate variability: a review”, Medical & Biological Engineering & Computing, vol. 44 (12), pp. 1031-1051, 2006.

G. Ernst, Heart Rate Variability, London: Springer-Verlag, 2014.

M. V. Kamath, M. A. Watanabe, A. R. M. Upton (Eds.), Heart Rate Variability (HRV) Signal Analysis: Clinical Applications, 1st ed., CRC Press Taylor&Francis Group, 2016.

S. Laborde, E. Mosley and J. F. Thayer, “Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research- Recommendations for Experiment Planning, Data Analysis, and Data Reporting”, Frontiers in Psychology, 8:213, 2017.

Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use, Task Force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology, European Heart Journal, vol. 17, pp. 354-381, 1996.

(2019) M.P. Tarvainen, J. Lipponen, J.-P. Niskanen, P. O. Ranta-aho, “Kubios (ver. 3.3)”, User’s guide. [Online]. Available: https://www.kubios.com/downloads/Kubios_HRV_Users_Guide.pdf.

L. Mourot, “CODESNA_HRV, a new tool to assess the activity of the autonomic nevrous system from heart rate variability”, Physical Medicine and Rehabilitation Research, vol. 3(1), pp. 1-6, 2018.

R. Bartels, L. Neumamm, T. Pecanha, S. Carvalho,” SinusCor: an advanced tool for heart rate variability analysis”, BioMed. Eng. OnLine, vol. 16(1), pp. 110-124, 2017.

L. Rodrá½·guez-Liñres, M.J. Lado, X.A. Vila, A.J. Mí©ndz, P.Guesta, “gHRV: Heart Rate Variability analysis made easy”, Computer Methods and Programs in Biomedicine, vol. 116(1), pp. 26-38, 2014.

C.A.G. Martí­nez, A.O. Quintana, X.A. Vila, M.J.L. Touriño, L. Rodrí­guez-Liñares, J.M.R. Presedo, A.J.M. Pení­n, Heart Rate Variability Analysis with the R package RHRV, Springer International Publishing, 2017.

(2019) HeartMath Institute. [Online]. Available: https://healthy-heart-meditation.com/heartmath-institute/

S. Pandey, W. Voorsluys, S. Niu, A. Khandoker and R. Buyya, “An autonomic cloud environment for hosting ECG data analysis services”, Future Generation Computer Systems, vol. 28, pp. 147-154, 2012.

A. Kalinichenko, S. Motorina, “Algorithms for ECG Analysis in Mobile Cardiac Monitoring Systems”, in Proc. of the 20th Conference of Open Innovations Association, pp. 112-117, April 2017.

O. Barquero-Pí©rez, T. Quintanilla, J. Garí­a-Muñoz, C. Soguero-Ruiz, M.R. Wilby, M de la Rosa, M Cabañas, I. Bravo, A. Garcí­a-Alberola and J. L. Rojo-ílvarez, “eLab: A Web-based Platform to Perform HRV and HRT Analysis and Store Cardiac Signals”, Computing in Cardiology, vol. 40, pp. 21-24, 2013.

J. Mohammed, C. H. Lung, A. Ocneanu, A. Thakral, C. Jones and A. Adler, “Internet of Things: Remote patient monitoring using web services and cloud computing” , IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom), and IEEE Cyber, Physical and Social Computing (CPSCom), Taipei, pp. 256-263, 2014.

R. N. Kirtana and Y. V. Lokeswari, “An IoT Based Remote HRV Monitoring System for Hypertensive Patients”, International Conference on Computer, Communication, and Signal Processing (ICCCSP), Chennai, India, pp. 1-6, 2017.

J. Garcí­a, J. D. Trigo, A. Alesanco, P. Serrano, J. Mateo, R. S. Istepatian, “Design and evaluation of wireless decision-support system for heart rate variability study in haemodialysis follow-up procedures”, Computer Methods and Programs in Biomedicine, vol. 88(3), pp. 273-282, 2007.

I. E. Akkus, R. Chen, I. Rimac, M. Stein, K. Satzke, A. Beck, P. Aditya, and V. Hilt, “SAND:Towards high-performance serverless computing”, In Proc. USENIX Annual Technical Conference, 2018, pp. 923-935.

E. Oakes, L. Yang, D. Zhou, and K. Houck, T. Harter, A. C. Arpaci-Dusseau and R. H. Arpaci-Dusseau, “SOCK: Rapid task provisioning with serverless-optimized containers”, In Proc. USENIX Annual Technical Conference, 2018, pp. 57-69.

B. Liston. 2017 (Update 2018) Resize Images on the Fly with AmazonS3, AWS Lambda, and Amazon API Gateway. [Online]. Available: https://aws.amazon.com/blogs/compute/resize-images-on-the-fly-with-amazon-s3-aws-lambda-and-amazon-api-gateway/.

S. Hendrickson, S. Sturdevant, T. Harter, V. Venkataramani, A. C. Arpaci-Dusseau, and R. H. Arpaci-Dusseau, “Serverless Computation with OpenLambda,” in Proc. 8th USENIX Workshop Hot Topics Cloud Computing (HotCloud), 2016, pp. 33-39.

M. Roberts. (2018). Serverless Architectures. [Online]. Available: https://martinfowler.com/articles/serverless.htm.

E. Eyk, A. Iosup, S.Seif, M. Thí¶mmes, “The SPEC Cloud Group’s Research Vision onFaaS and Serverless Architectures”, in Proceedings of the 2nd International Workshop on Serverless Computing WoSC’17, 2017.

T. Taylor, “Top 8 tools to use when working with serverless computing”, TechGenix, 2018.

P. Aditya, I.E. Akkus, A. Beck, R. Chen, V. Hilt, I. Rimac, K. Satzke and M. Stein, “Will Serverless Computing Revolutionize NFV?”, Proceedings of the IEEE, vol. 107, No. 4, pp. 667-678, 2019.

K&C Team, “Serverless Architecture for Modern Apps: Stacks Providers&Caveats”, October 9, 2018 (Updated:April 19, 2019).

D. Poccia, AWS Lambda in Action, Manning Publicatios Co, 2017.

C. Gurturk, Building Serverless Architectures, Birmingham, UK: Packt Publishing, 2017.

M. Martinis, A. Knežević, G. KrstaÄić, and E. Vargović, “Changes in the Hurst exponent of heart beat intervals during physical activities”, Physical Review E, vol. 70(1), Art. No.012903, July 2004.

H. Salat, R. Murcio, E. Arcaute, “Multifractal methodology”, Physica A: Statistical Mechanics and its Applications, vol. 473, pp. 467-487, May 2017.

C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley and A.L. Goldberger. “Mosaic Organization of DNA nucleotides”, Physical Review E, vol. 49 (2), pp.1685-1689, 1994.

J.W. Kantelhardt, S.A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, H.E. Stanley, “Multifractal detrended fluctuation analysis of nonstationary time series”, Physica A: Statistical Mechanics and its Applications, vol. 316 (1-4), pp. 87-114, 2002.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).