Detection of Myocardial Infarction Using Statistics features of Electrocardiographic ST-Segment and Discriminant Analysis

  • Dewi Cahya Fitri Universitas Sebelas Maret
  • nuryani nuryani Jurusan Fisika, Universitas Sebelas Maret
  • Anto Satriyo Nugraha PTIK BPPT


This article describes the detection of myocardial infarction using the statistical mean features, including the mean, median, and standard deviation. The classification used is the discriminant analysis method which is implemented using matlab software. The ECG signal obtained from the device is then processed. After that, the feature extraction is carried out. The results of the extraction is normalized so that all patient data have the same standard in amplitude wave magnitude. After normalization, the data will be used as input for discriminant analysis. In this article we try to use the mean, median, and standard deviation features. In this experiment using 15 leads consisting of 12 conventional leads and 3 posterior leads, the addition of these 3 leads has the advantage of determining the performance results obtained. Percentage of accuracy performance, the best percentage of accuracy performance is 97.73% with the mean feature. This experiment tries to compare the features of mean and standard deviation, mean and median, standard deviation and median, and mean, median, and standard deviation. The combined experiment shows that the best accuracy performance percentage value is 98.84% with standard deviation and median features.


S. Boateng dan T. Sanborn, “Acute Myocardial Infarction,” Dis Mon, Vol. 59, No. 3, hal. 83-96, 2013.

E.M. Antman, D.T. Anbe, P.W. Armstrong, E.R. Bates, L.A. Green, M. Hand, dkk., “ACC/AHA Guidelines for the Management of Patient with ST Elevation Myocardial Infarction,” Circulation, Vol. 110, No. 5, hal. 588-636, 2004.

(2011) Kementerian Kesehatan Republik Indonesia website, [Online],, tanggal akses 19-Nov-2020.

C. Smeltzer, B.G. Bare, Brunner, dan Suddarth, Textbook of Medical Surgical Nursing, 11th ed., Philadelphia, USA: Wolters Kluwer, 2010.

D.D. Ignativicius dan M.L. Workman, Medical Surgical Nursing: Critical Thinking for Collaborative Care, 4th ed., St. Louis, USA: Elsevier Saunders, 2006.

A. Heru, “Desain Alat Deteksi Dini dan Mandiri Aritmia,” J. Teknol. Manaj. Informatika, Vol. 6, No. 3, hal. 494-502, 2008.

R.K. Tripathy, A. Bhattacharyya, dan R.B. Pachori, “Localization of Myocardial Infarction from Multi-Lead ECG Signals Using Multiscale Analysis and Convolutional Neural Network,” IEEE Trans. Biomed. Eng., Vol. 19, No. 23, hal. 11437-11448, 2019.

W.J. Brady, V. Hwang, R. Sullivan, N. Chang, C. Beagle, C.T. Carter, M.L. Martin, dan T.P. Aufderheide, “A Comparison of 12- and 15-Lead ECGs in ED Chest Pain Patients: Impact on Diagnosis, Therapy, and Disposition,” Am. J. Emerg. Med., Vol. 18, No. 3, hal. 239-243, 2000.

H. Munirwan dan R. Pebriana, “Evolusi EKG pada STEMI dengan Gelombang Q Patologis: Haruskah Menunda Terapi?,” J. Ked. N. Med., Vol. 3, No. 1, hal. 21-29, 2020.

A. Ranjbar, B. Sohrabi, S.R.S. Ebrahimi, S. Ghaffari, B. Kazemi, N. Aslanabadi, B. Seyvani, dan R. Hajizadeh, “The Association between T Wave Inversion in Leads with ST-Elevation and Patency of the Infarct-related Artery,” BMC Cardiovascular Disorders, Vol. 21, No. 1, hal. 1-6, 2021.

L. Sun dan Y. Lu, “ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection,” IEEE Trans. Biomed. Eng., Vol. 59, No. 12, hal. 3348-3356, 2012.

N. Liu, L. Wang, Q. Chang, Y. Xing, dan X. Zhou, “A Simple and Effective Method for Detecting Myocardial Infarction Based on Deep Convolutional Neural Network,” J. Med. Imaging Health Inf., Vol. 8, No. 7, hal. 1508-1512, 2018.

X. Lun, Z. Yu, T. Chen, F. Wang, dan Y. Hou, “A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals,” Front. Hum. Neurosci., Vol. 14, hal. 1–14, 2020.

R. Bousseljot, D. Kreiseler, dan A. Schnabel, “Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet,” Biomedizinische Technik, Vol. 40, No. s1, hal. 317-318, 1998.

D.H. Lee, J.W. Park, J. Choi, A. Rabbi, dan R.F. Rezai, “Automatic Detection of Electrocardiogram ST Segment: application in Ischemic Disease Diagnosis,” Int. J. Adv. Comput. Sci. Appl., Vol. 4, no. 2, hal. 150-155, 2014.

B. Sartono, F.M. Affendi, U.D. Syafitri, I..M. Sumertajaya, dan Y. Anggraeni, Analisis Peubah Ganda, Bogor, Indonesia: IPB Press, 2003.

R.L. Tatham, J.F. Hair, R.E. Anderson, dan W.C. Black, Multivariate Data Analysis, Hoboken, USA: Prentice Hall, 1998.

W. Zhu, N. Zeng, dan N. Wang, “Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementation,” NESUG Proceeding: Health Care and Life Sciences, 2010, hal. 1-9.

How to Cite
Dewi Cahya Fitri, nuryani, nuryani, & Anto Satriyo Nugraha. (2021). Detection of Myocardial Infarction Using Statistics features of Electrocardiographic ST-Segment and Discriminant Analysis. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 10(3), 243-248.