Extending the sana mobile healthcare platform with features providing ecg analysis

Katerina Tsampi, Spyros Panagiotakis, Elias Hatzakis, Emmanouil Lakiotakis, Georgia Atsali, Kostas Vassilakis, George Mastorakis, Constandinos X. Mavromoustakis, Athanasios Malamos

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The great development of technology recently provides innovations that improve everyday life. The major benefit of it is that medicine is also affected, so better healthcare can be provided. In that context, it can be critical for patients who suffer from chronic heart diseases to have in their availability a system that can monitor and analyse their electrocardiogram (ECG) displaying either normal or abnormal findings. The current chapter describes such a system that uploads, stores, processes and displays an ECG, calculating certain ECG findings necessary for doctors to make a diagnosis. To this end, the SANA mobile healthcare platform, with its OpenMRS open source enterprise electronic medical record system, has been chosen and extended in this work for storing, processing and displaying the ECG data. OpenMRS provides a user-friendly interface and a database for collecting medical big data. Analysis of ECG signals is leveraged by the Physionet toolkit. Physionet contains many ECG databases and the WFDB software for processing ECG signals. According to the scenario we have processed, an ECG is uploaded onto OpenMRS platform using a mobile device or any other Internet-enabled device and is stored in the database that OpenMRS uses. Then, ECG signal is filtered using a finite impulse response (FIR) filter to remove noise and using WFDB functions it is processed so certain intervals are determined. Finally, with the appropriate algorithms specific ECG findings are calculated. When the procedure completes, the results are stored into the database using SQL Queries. Using an HTML Form results and graphs are integrated into the OpenMRS website highlighting abnormal values with red color. Authorized users can have access to this information through any web browser.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer India
Number of pages33
Publication statusPublished - 2018

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520


  • Big data
  • ECG signal processing
  • Electrocardiogram
  • Healthcare applications
  • OpenMRS platform


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