International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies


:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

ISSN 2228-9860
eISSN 1906-9642



  • E3Graphy: A Novel Integrated Model for Bio-signals Acquisition & Disease Detection

    Muhammad Amjid, Hammad Hussain, Mumtaz Ali, Bilal Khan, Shahab Haider (Department of Computer Science, City University of Science and Information Technology, Peshawar, Pakistan).

    Discipline: Computer Science and Information Technology

    ➤ FullText

    doi: 10.14456/ITJEMAST.2023.21

    Keywords:Bio-Signals Acquisition; ECG; EEG; EMG; Disease Detection; Health Monitoring Model.

    Recent technological advancements and the escalating number of Internet of Things-enabled systems have improved health monitoring to a significant level. The human body emits various bio-signals that include Electrocardiograph (ECG), Electromyography (EMG), and Electroencephalogram (EEG). These bio-signals are observed to monitor health status and disease diagnosis. However, noise and power line interference adversely affect the accuracy. To that end, this paper proposes a novel portable health monitoring model, namely, E3Graphy that aims to bridge the communication gap between doctors and patients, thereby, enhancing diagnostics. E3Grpahy integrates the features of ECG, EMG, and EEG in a less power-consuming smaller-sized monitoring model, which comprises sensors, a microcontroller, a Bluetooth module, and a mobile application. Moreover, E3Graphy employs machine learning techniques, such as Support Vector Machine, Na?ve Bayes, Neural Network, and Extreme Gradient Boosting to enable early disease detection. Results demonstrate that E3Graphy achieves an accuracy of 90.58% in ECG and 98.28% in EEG, and classifies diseases for ECG with a confidence score of 70.47%.

    Paper ID: 14A1U

    Cite this article:

    Amjid M., Hussain H., Ali M., Khan B., Haider S. (2023). E3Graphy: A Novel Integrated Model for Bio-signals Acquisition & Disease Detection. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 14(1), 14A1U, 1-14. http://TUENGR.COM/V14/14A1U.pdf DOI: 10.14456/ITJEMAST.2023.21


  1. Abdulhamit Subasi, E. Y. (2019). EMG Signal Classification Using Discrete Wavelet Transform and Rotation Forest. International Conference on Medical and Biological Engineering.
  2. Ahamed, M. A., Ahad, M. A.-U., Sohag, M. H., & Ahmad, M. (2015). Development of low cost wireless biosignal acquisition system for ECG EMG and EOG. IEEE 2nd International Conference on Electrical Information and Communication Technology.
  3. Al-Busaidi, A. M., & Khriji, L. (2013). Digitally filtered ECG signal using low-cost microcontroller. International Conference on Control, Decision and Information Technologies (CoDIT). Hammamet, Tunisia.
  4. Alforidi, Ahmad; Aslam, Dean M. (2018). Fabric-Embedded EEG/ECG/EMG Micro-Systems Monitoring Smart-Horne-Occupants' Health/Disease by Smartphones. IEEE International Conference on Electro/Information Technology (EIT) .
  5. Arulananth, T. S., & Shilpa, B. (2017). Fingertip based heart beat monitoring system using embedded systems. International conference of Electronics, Communication and Aerospace Technology (ICECA) .
  6. Barrett, S.F. (2013). Arduino Microcontroller Processing for Everyone. Morgan Claypool Publishers .
  7. Bio Protech ECG Electrode specification. (n.d.). (BIO PROTECH INC.) Retrieved from
  8. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., et al. (2013, Sep 1). API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases.
  9. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., et al. (2013). API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, 108-122.
  10. Chen, T., & He, T. (2020). xgboost: eXtreme Gradient Boosting.
  11. Dong-Mei, H., Yi, Y., & Zheng, W. (2009, September 7). Measurement System for Surface Electromyogram and Handgrip Force Based on LabVIEW. World Congress on Medical Physics and Biomedical Engineering, 67-70.
  12. Goldberger, Amaral, A. L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.
  13. Gope, D., Schlais, D. J., & Lipasti, M. H. (2017). Architectural Support for Server-Side PHP Processing. 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture . Toronto, ON, Canada.
  14. Han, J. P. (2011). Data mining: concepts and techniques. Elsevier.
  15. Iqbal, A., Aftab, S., Ali, U. (2019). Performance analysis of machine learning techniques on software defect prediction using NASA datasets. International Journal of Advanced Computer Science and Applications, 10 (5), 300-308.
  16. Islam, M. K., Haque, A. N., Tangim, G., Ahammad, T., & Khondokar, M. R. (2012). Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools . International Journal of Computer and Electrical Engineering, 4.
  17. Jani, A. B., Bagree, R., & Roy, A. K. (2017). Design of a low-power, low-cost ECG & EMG sensor for wearable biometric and medical application. IEEE SENSORS, 1-3.
  18. Jin, H., Kim, S., and Kim, J.. (2014). Decision factors on effective liver patient data prediction. International Journal of Bio-Science and Bio-Technology, 6 (4), 167-178.
  19. Jothi, N., Rashid, N. A., & Hussain, W. (2015). Data Mining in healthcare - A Review. The Third Information Systems International Conference. Penang Malaysia.
  20. Kaniusas, E. (2012). Biomedical Signals and Sensors I: Linking Physiological Phenomena and Biosignals. Springer Science & Business Media.
  21. Khan, B., Rashid N., Fazal M., Ghulam A., and Sunghwan K.. (2020). An empirical evaluation of machine learning techniques for chronic kidney disease prophecy. IEEE Access, 8, 55012-55022.
  22. Khan, B., Rashid, N., Mumtaz, A., Muhammad, A., and Nazir, J. (2019). Machine learning approaches for liver disease diagnosing. International Journal of Data Science and Advanced Analytics, 1(1), 27-31.
  23. Khan, S., Ullah, R., Khan, A., Wahab, N., Bilal, M., & Ahmed, M. (2016). Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM). Biomedical optics express, 7 (6), 2249-2256.
  24. Leijdekkers, P., & Gay, V. (2008). A self-test to detect a heart attack using a mobile phone and wearable sensors. 21st IEEE International Symposium on Computer-Based Medical Systems, 93-98.
  25. Menzies, T., Dekhtyar, A., Distefano J., and Greenwald J.. (2007). Problems with precision: a response to "comments on 'data mining static code attributes to learn defect predictors'. IEEE Transactions on Software Engineering, 33 (9), 637-640.
  26. Nahar, N. and Ara, F.. (2018). Liver disease prediction by using different decision tree techniques. International Journal of Data Mining & Knowledge Management Process, 8(2), 01-09.
  27. Naseem, R., Khan, B., Shah, M. A., Wakil, K., Khan, A., Alosaimi, W., ... & Alouffi, B. (2020). Performance assessment of classification algorithms on early detection of liver syndrome. Journal of Healthcare Engineering.
  28. Nyni, K., Vincent, L. K., Varghese, L., Liya, V., Johny, A. N., & Yesudas, C. (2017). Wireless health monitoring system for ECG, EMG and EEG detecting. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) , 1-5.
  29. Omran, D. A. E. H., Awad, A. H., Mabrouk, M. A. E. R., Soliman, A. F., & Aziz, A. O. A. (2015). Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease. Asian Pacific Journal of Cancer Prevention, 16(1), 381-385
  30. Parameshwari, B., Madhurya, B., & Rao, K. (2013). Preparation of Papers a Cost Effective Prototype for Electrooculogram for Effective Eye Tracking. International Journal of Innovation, Management and Technology, 4(5).
  31. Prasad, A. S., & N, K. (2019). ECG MONITORING SYSTEM USING AD8232 SENSOR . Proceedings of the Fourth International Conference on Communication and Electronics Systems.
  32. Pyakillya, B., Kazachenko, N., & Mikhailovsky, N. (2017). Deep Learning for ECG Classification. IOP Conf. Series: Journal of Physics.
  33. Reas, C., & Fry, B. (2007). Processing: a programming handbook for visual designers and artists. MIT Press.
  34. Saritas, M. M. (2019). Performance analysis of ANN and naive Bayes classification algorithm for data classification. International Journal of Intelligent Systems and Applications in Engineering, 7 (2), 88-91.
  35. Smolka, J., Zurek, S., Lukasik, E., & Skublewska-Paszkowska, M. (2017). Heart rate estimation using an EMG system integrated with a motion capture system. IEEE International Conference on Electromagnetic Devices and Processes in environments.
  36. Tong, H., Liu, B., and Wang, S.. (2018). Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning. Information and Software Technology, 96, 94-111.
  37. Winkler, P. F. (2007). Arduino Workshop.
  38. Wu, C., Kao, S. C., Shih, C. H., & Kan, M. H. (2018). Open data mining for Taiwan’s dengue epidemic. Acta tropica, 183, 1-7.
  39. Xiong1, Z., Nash, M. P., Cheng, E., V, V., Fedoro, Stiles, M. K., et al. (2018). ECG Signal Classification for the Detection of Cardiac Arrhythmias Using a Convolutional Recurrent Neural Network. Institute of Physics and Engineering in Medicine.
  40. Young, J., Roy, H. S., & Young, J. (2007). Patent No. 7,284,239. U.S.

Other issues:


Call-for-Scientific Papers
Call-for-Research Papers:
ITJEMAST invites you to submit high quality papers for full peer-review and possible publication in areas pertaining engineering, science, management and technology, especially interdisciplinary/cross-disciplinary/multidisciplinary subjects.

To publish your work in the next available issue, your manuscripts together with copyright transfer document signed by all authors can be submitted via email to Editor @ (please see all detail from Instructions for Authors)

Publication and peer-reviewed process:
After the peer-review process, articles will be on-line published in the available next issue. However, the International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies cannot guarantee the exact publication time as the process may take longer time, subject to peer-review approval and adjustment of the submitted articles.