Research Article
BibTex RIS Cite

Design of a Microcomputer Based Realtime ECG Holter Device

Year 2017, Volume: 5 Issue: 3, 146 - 156, 01.10.2017
https://doi.org/10.21541/apjes.335275

Abstract

The growing aging population rate in our country and
all over the world and increase in heart diseases lead to some requirements; in
fact, it's indispensable to keep activities of this vital organ under control
and observe all the effects of during and pre-treatment process. The primary
goal of this study is to design a specifically developed device that will
facilitate human life by any means of the specifications and implementation of
portable ECG Holter device with open source software and upgradeable embedded
system. The study that we have conducted consists of 3 phases; The 1st phase
made it suitable to signal processing stage by compiling EKG signals with the
aid of bioinstrumentation amplifier circuit that we developed. Afterwards, bioinstrumentation
amplifier and signals raised by 205 times. In order to suppress network noise,
50HZ notch filter was implemented on ECG Signals and a Butterworth filter with
the bandwidth of 0.01-130 Hz was used. In the 2nd phase, analog ECG sign,
provided by the participants was digitized by using analog digital converters. It
was linked up with embedded system cards via communication protocols. Three
different types of embedded system cards and signal processing algorithm were
setup and the interface that we designed was developed in Python language owing
to a great number of libraries. However, it was replaced by another programming
in C++ language since this language did not allow signal processing algorithm
function well due to lack of operating speed. In the 3rd phase, ECG data was
recorded after 10 different participants moved upstairs and downstairs at
intervals of 100 sec, followed by breaks of 3 times.  Later on, the Raspberry Pi, Beaglebone and
Odroid embedded system cards were compared in terms of speed differences and
performances, and also consequences were analyzed. Since the sampling rate with
Beaglebone didn't exceed 35 Hz, it was determined that this was inappropriate
for the use of ECG. The sampling rate with Raspberry Pi remained around 80 Hz
and it was confirmed that this could be used only for checking the pulse. As
far as Odroid is concerned, since sampling rate went up to around 250 Hz, It
was assigned to be the best microcomputer.

References

  • Mandıracıoğlu A, “ Demographic chcracteristics of the elderly population in Turkey and the world, Ege Tıp Dergisi, 49 (3) Ek: 39-45, 2010.
  • J. McKay and G. A. Mensah, The atlas of heart disease and stroke. Geneva: World Health Organization, 2005.
  • Webster, J. (1984). Reducing Motion Artifacts and Interference in Biopotential Recording. IEEE Transactions on Biomedical Engineering, BME-31(12), pp.823-826.
  • Levy, R., Labhasetwar, V., Strickberger, S., Underwood, T. and Davis, J. (1996). Controlled release implant dosage forms for cardiac arrhythmias: Review and perspectives. Drug Delivery, 3(3), pp.137-142.
  • Yazgan, E. (1996). Tıp elektroniği. İstanbul: İTÜ.
  • J. G. Webster and J. W. Clark, Medical Instrumentation: Applications and design. New York: John Wiley & Sons, 1998.
  • Khobragade, K. and Deshmukh, R. (1999). ECG analysis using wavelet transforms. Computer Standards & Interfaces, 20(6-7), p.466.
  • Fratini, A., Sansone, M., Bifulco, P. and Cesarelli, M. (2015). Individual identification via electrocardiogram analysis. BioMedical Engineering OnLine, 14(1).
  • HAYIT, Tolga; ERGÜN, ‘Sağlık Sektöründe Geliştirilen Mobil Uygulamaların İncelenmesi Ve Mobil Cihazlar İçin Hasta Takip Uygulaması’ AJIT-e: Online Academic Journal of Information Technology . Spring2016, Vol. 7 Issue 23, p97-114. 18p.
  • L. Lavagno, “Embedded Systems,” Embedded Systems Handbook Industrial Information Technology, 2005.
  • Türker, G.F., Güler, İ., “Farksal Yalıtılmış EKG Tasarımı ve Uygulaması”, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 16-3, 264-268 (2012).
  • Ti.com. (2017). INA128 Precision, 130-dB CMRR, 700-µA, Low-Power, Instrumentation Amplifier | TI.com. [online] Available at: http://www.ti.com/product/INA128 [Accessed 16 Aug. 2017].
  • Furno, G. and Tompkins, W. (1983). A Learning Filter for Removing Noise Interference. IEEE Transactions on Biomedical Engineering, BME-30(4), pp.234-235.
  • Levkov, C., Michov, G., Ivanov, R. and Daskalov, I. (1984). Subtraction of 50 Hz interference from the electrocardiogram. Medical & Biological Engineering & Computing, 22(4), pp.371-373.
  • Schaumann, R., Xiao, H., Van Valkenburg, M., Van Valkenburg, M. and Van Valkenburg, M. (2011). Analog filter design. New York: Oxford University Press.
  • Ahlstrom, M. and Tompkins, W. (1985). Digital Filters for Real-Time ECG Signal Processing Using Microprocessors. IEEE Transactions on Biomedical Engineering, BME-32(9), pp.708-713.
  • Lynn, P. (1971). Recursive digital filters for biological signals. Medical & Biological Engineering, 9(1), pp.37-43.
  • Lian, J., Wang, L. and Muessig, D. (2011). A Simple Method to Detect Atrial Fibrillation Using RR Intervals. The American Journal of Cardiology, 107(10), pp.1494-1497.
  • M. Fowler and C. Kobryn, UML distilled: a brief guide to the standard object modeling language ; Boston, MA: Addison-Wesley, 2009.
  • Deanfield, J. (1987). Holter monitoring in assessment of angina pectoris. The American Journal of Cardiology, 59(7), pp.C18-C22. Raspberry Pi. (2017).
  • Raspberry Pi - Teach, Learn, and Make with Raspberry Pi. [online] Available at: https://www.raspberrypi.org/ [Accessed 16 Aug. 2017].
  • Beagleboard.org. (2017). BeagleBoard.org - bone. [online] Available at: http://beagleboard.org/bone [Accessed 16 Aug. 2017].
  • Hardkernel.com. (2017). ODROID | Hardkernel. [online] Available at: http://www.hardkernel.com/main/main.php [Accessed 16 Aug. 2017].
  • T.Kantar, Ö.Köseoğlu ‘Analysis of Diseases from ECG Signal’ Biyomut 2014, pp 257-260.

Gömülü Sistem Tabanlı EKG Holter Cihazının Tasarlanması

Year 2017, Volume: 5 Issue: 3, 146 - 156, 01.10.2017
https://doi.org/10.21541/apjes.335275

Abstract

Ülkemizde ve dünyada nüfusun giderek
yaşlanması ve kalp rahatsızlıklarının artması, bu hayati organımızın
faaliyetlerini sürekli kontrol altında tutma, tedavi sürecinde ve öncesinde tüm
etkileri gözlemleme gibi ihtiyaçları doğurmaktadır. Çalışmanın temel amacı,
açık kaynak kodlu, geliştirilmeye açık gömülü sistem tabanlı taşınabilir bir EKG
Holter cihazını gerçekleştirmektir. Yapmış olduğumuz çalışma üç aşamadan
oluşmaktadır: İlk aşamada, bireyin Elektrokardiyografi (EKG) sinyalleri tasarladığımız
biyoenstrümantasyon yükseltici devresi ile toplanarak yaklaşık 200 kat
kuvvetlendirilmiş ve sonraki sinyal işleme aşamasına uygun hale getirilmiştir.
Şebeke gürültüsünü bastırmak üzere EKG sinyallerine 50 Hz ‘lik çentik filtre ve
bant genişliği 0,01 – 130 Hz arasında olan bant geçiren filtre uygulanmıştır.
İkinci aşamada ise analog EKG işareti Analog Dijital Çeviriciler kullanılarak
sayısallaştırılmıştır. Haberleşme protokolleri yazılarak gömülü sistem
kartlarıyla bağlantısı kurulmuştur. Üç farklı gömülü sistem kartı ile sinyal
işleme algoritmaları uygulanacak hale getirilmiştir. Tasarladığımız arayüz ilk
olarak çok sayıda kütüphane desteği olması sebebiyle Python dilinde
geliştirilmiş. Ancak bu dilin yazdığımız sinyal işleme algoritmasını çalıştırma
hızı yeterli olmadığından C++ dilinde programlama yapılmaya geçilmiştir. Üçüncü
aşamada, 10 farklı katılımcıdan 100 sn. süresince 3’er adet a- dinlenme halinde
ve b- merdiven inme çıkma hareketinin ardından EKG verileri kaydedilmiştir.
Daha sonra, Raspberry Pi, Beaglebone ve Odroid gömülü sistem kartları
arasındaki hız farkları ve performansları analiz edilerek karşılaştırılması
yapılmış ve sonuçlar incelenmiştir. Beaglebone kullanılarak örnekleme hızı 35
Hz’i geçemediğinden EKG için kullanımının uygun olmadığı saptanmıştır.
Raspberry Pi ile örnekleme hızı 80 Hz civarında kalmakta ve sadece nabız hesabı
için kullanılabileceği saptanmıştır. Odroid’ de ise örnekleme hızı 250 Hz
civarına çıkabildiğinden ECG analizi için en uygun mikrobilgisayar olarak
belirlenmiştir.

References

  • Mandıracıoğlu A, “ Demographic chcracteristics of the elderly population in Turkey and the world, Ege Tıp Dergisi, 49 (3) Ek: 39-45, 2010.
  • J. McKay and G. A. Mensah, The atlas of heart disease and stroke. Geneva: World Health Organization, 2005.
  • Webster, J. (1984). Reducing Motion Artifacts and Interference in Biopotential Recording. IEEE Transactions on Biomedical Engineering, BME-31(12), pp.823-826.
  • Levy, R., Labhasetwar, V., Strickberger, S., Underwood, T. and Davis, J. (1996). Controlled release implant dosage forms for cardiac arrhythmias: Review and perspectives. Drug Delivery, 3(3), pp.137-142.
  • Yazgan, E. (1996). Tıp elektroniği. İstanbul: İTÜ.
  • J. G. Webster and J. W. Clark, Medical Instrumentation: Applications and design. New York: John Wiley & Sons, 1998.
  • Khobragade, K. and Deshmukh, R. (1999). ECG analysis using wavelet transforms. Computer Standards & Interfaces, 20(6-7), p.466.
  • Fratini, A., Sansone, M., Bifulco, P. and Cesarelli, M. (2015). Individual identification via electrocardiogram analysis. BioMedical Engineering OnLine, 14(1).
  • HAYIT, Tolga; ERGÜN, ‘Sağlık Sektöründe Geliştirilen Mobil Uygulamaların İncelenmesi Ve Mobil Cihazlar İçin Hasta Takip Uygulaması’ AJIT-e: Online Academic Journal of Information Technology . Spring2016, Vol. 7 Issue 23, p97-114. 18p.
  • L. Lavagno, “Embedded Systems,” Embedded Systems Handbook Industrial Information Technology, 2005.
  • Türker, G.F., Güler, İ., “Farksal Yalıtılmış EKG Tasarımı ve Uygulaması”, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 16-3, 264-268 (2012).
  • Ti.com. (2017). INA128 Precision, 130-dB CMRR, 700-µA, Low-Power, Instrumentation Amplifier | TI.com. [online] Available at: http://www.ti.com/product/INA128 [Accessed 16 Aug. 2017].
  • Furno, G. and Tompkins, W. (1983). A Learning Filter for Removing Noise Interference. IEEE Transactions on Biomedical Engineering, BME-30(4), pp.234-235.
  • Levkov, C., Michov, G., Ivanov, R. and Daskalov, I. (1984). Subtraction of 50 Hz interference from the electrocardiogram. Medical & Biological Engineering & Computing, 22(4), pp.371-373.
  • Schaumann, R., Xiao, H., Van Valkenburg, M., Van Valkenburg, M. and Van Valkenburg, M. (2011). Analog filter design. New York: Oxford University Press.
  • Ahlstrom, M. and Tompkins, W. (1985). Digital Filters for Real-Time ECG Signal Processing Using Microprocessors. IEEE Transactions on Biomedical Engineering, BME-32(9), pp.708-713.
  • Lynn, P. (1971). Recursive digital filters for biological signals. Medical & Biological Engineering, 9(1), pp.37-43.
  • Lian, J., Wang, L. and Muessig, D. (2011). A Simple Method to Detect Atrial Fibrillation Using RR Intervals. The American Journal of Cardiology, 107(10), pp.1494-1497.
  • M. Fowler and C. Kobryn, UML distilled: a brief guide to the standard object modeling language ; Boston, MA: Addison-Wesley, 2009.
  • Deanfield, J. (1987). Holter monitoring in assessment of angina pectoris. The American Journal of Cardiology, 59(7), pp.C18-C22. Raspberry Pi. (2017).
  • Raspberry Pi - Teach, Learn, and Make with Raspberry Pi. [online] Available at: https://www.raspberrypi.org/ [Accessed 16 Aug. 2017].
  • Beagleboard.org. (2017). BeagleBoard.org - bone. [online] Available at: http://beagleboard.org/bone [Accessed 16 Aug. 2017].
  • Hardkernel.com. (2017). ODROID | Hardkernel. [online] Available at: http://www.hardkernel.com/main/main.php [Accessed 16 Aug. 2017].
  • T.Kantar, Ö.Köseoğlu ‘Analysis of Diseases from ECG Signal’ Biyomut 2014, pp 257-260.
There are 24 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Ahmet Yesevi

Muhammed Güler This is me

Mustafa Zahid Yıldız This is me

Publication Date October 1, 2017
Submission Date August 18, 2017
Published in Issue Year 2017 Volume: 5 Issue: 3

Cite

IEEE A. Yesevi, M. Güler, and M. Z. Yıldız, “Design of a Microcomputer Based Realtime ECG Holter Device”, APJES, vol. 5, no. 3, pp. 146–156, 2017, doi: 10.21541/apjes.335275.