FILTERING DATA DISKRIT ELEKTROKARDIOGRAM UNTUK PENENTUAN PQRST DALAM SATU SIKLUS

Sabar Setiawidayat, Suci Imani Putri

Sari


Dalam artikel ini telah dapat direpresentasikan data nilai-nilai amplitude puncak PQRST dalam tiap siklus. Gelombang kontinyu sinyal Elektrokardiogram (ECG) hasil pemeriksaan, di sampling pada frekuensi 250 Hz sehingga diperoleh data diskrit dengan durasi step 0.004 detik. Durasi waktu peak R to peak R (dR) digunakan sebagai periode waktu tiap cycle. Pergeseran mundur durasi 1.5dR dari RN+1 akan diperoleh titik awal siklus
sedangkan pergeseran mundur durasi 0.5dR dari RN+1 akan didapatkan titik akhir siklus. Peak P digunakan untuk
merepresentasikan keadaan depolarisasi Atrium, QRS digunakan untuk menunjukkan depolarisasi ventrikel dan peak T digunakan untuk menunjukkan kondisi repolarisasi dalam otot-otot Jantung. Data diskrit dari Physionet MIT-BIH dan hasil pengukuran sendiri memakai ECGd 12-lead digunakan sebagai data untuk memperoleh nilai peak PQRST dalam tiap siklus

Teks Lengkap:

PDF (English)

Referensi


Ali Zifan, et.al, 2005. Automated ECG segmentation

using piecewise derivative dynamic time

warping, International journal of Biological

and life science 1:3 2005

Andreas S, et.al, 1998. Analysis of beat to beat

variability of frequency contens in the

Electrocardiogram using two-dimensional

Fourier Transform. IEEE transactions on

Biomedical Engineering, vol.45, No.2,

February 1998

Ashish Birle, Suyog Malviya, Deepak Mittal, 2015.

A novel technique of R-peak detection for

ECG signal analysis : variable threshold

method. International Journal of Advanced

Research in Electronics and Communication

Engineering (IJARECE), vol.4, issue 5, May

David Prutchi, Michael Norris. 2005. Design and

Development of Medical Electronic

Instrument. A John Wiley & Sons, Inc.,

Publication

Deboleena S and Madhuchhanda M, 2012. R-peak

detection algorithm for ECG using double

difference and RR interval processing.

SciVerse ScienceDirect Elsevier, Procedia

Technology 4 (2012) 873-877

FU Huwez, PW Macfarlane, 2003. Assesment of

selected ECG voltage criteria for

abnormality in Eccentric and Concentric left

ventricular hypertrophy. IEEE Computer in

Cardiology, 2003;30:57-59

Guyton. Arthur & Hall.E , 2008, Textbook of

Medical Physiology, 11th edition, Elsevier,

SingaporeHussain A Jaber AL, Ziarjawey and Cankaya, 2015.

Heart rate monitoring and PQRST detection

based on graphical user interface with

Matlab. International Journal of Information

and Electronics Engineering, vol.5, No.4,

July 2015

Kamalapriya M and Renulakshmi R, 2012.

Electrrocardiogram signal analysis using

zoom FFT. IEEE Biosignal and biorobotics

conference (BRC), 2012

Mohammad Rakibul Islam, et.all, 2015. Arrhythmia

detection technique using basic ECG

parameters. International journal of

Computer Applications (0975-8887) vol.119,

No.10,June 2015

Muttaqin A Ahmad, Budi Yuli S, 2009. Pocket ECG,

“How to learn ECG from zero”, Penerbit

Intan Cendikia, Yogyakarta. ISBN

No.979985718-3

PA Otubu, For the Realisation of the design of

Electrocardiogram for the Monitoring of the

Physiology of Human Heart, Journal of

Engineering and Applied Sciences

(11):856-860, 2008. ISSN:1816-

X,@Medwell Journals, 2008

Rashid GA, Mohammad AT, 2015. ECG based

detection of left ventricular hypertrophy

using higher order statistics. IEEE 2015 23rd

Iranian Conference on Electrical Engineering

(ICEE)

Sabar S, Rasjad I, Djanggan S, Setyawan S, Using

Discrete data of ECG in the Numerical and

Spectral forms, International Journal of

Electrical & Computer Sciences, IJECSIJENS, vol.15, No.03, June 2015

Sabar S, Rasjad I, Djanggan S, Setyawan S,

Determining the ECG 1 cycle wave using

dicrete data, Journal of Theoretical and

Applied Information Technology, Jatit,

vol.88, No.1, June 2016

Sachin Singh, Netaji Gandhi, Pattern analysis of

different ECG signal using Pan-Tompkin’s

algorithm, International Journal on Computer

Science and Engineering (IJCSE), vol.02,

no.07, 2010, 2502-2505

Thulasi Prasad S & Varadarajan S, 2015. Analysis of

ST-Segment abnormalitiesin ECG using

signal block averaging Technique.

International Journal of Advanced Research

in Computer and Communication

Engineering, vol.4, Issue 2, February 2015

Vivek SC, Durgesh KM, PB Patil, 2011. Assessment

of selected Electrocardiogram voltage

criteria for left ventricular hypertrophy by

using SPSS. IEEE First International

Conference on Informatics and

Computational Intelligence, 978-0-7695-

-6/11. DOI 10.1109/ICI.2011.69


Refbacks

  • Saat ini tidak ada refbacks.