Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification-Based Feature Selection

Seyed Ataddin Mahmoudinejad, Naser Safdarian

DOI: DOI: 10.4103/jmss.JMSS_12_20

Abstract


Background: Cardiovascular disease (CVD) is the first cause of world death, and myocardial infarction (MI) is one of the five primary disorders of CVDs which the patient electrocardiogram (ECG) analysis plays a dominant role in MI diagnosis. This research aims to evaluate some extracted features of ECG data to diagnose MI. Methods: In this paper, we used the Physikalisch-Technische Bundesanstalt database and extracted some morphological features, such as total integral of ECG, integral of the T-wave section, integral of the QRS complex, and J-point elevation from a cycle of normal and abnormal ECG waveforms. Since the morphology of healthy and abnormal ECG signals is different, we applied integral to different ECG cycles and intervals. We executed 100 of iterations on a 10-fold and 5-fold cross-validation method and calculated the average of statistical parameters to show the performance and stability of four classifiers, namely logistic regression (LR), simple decision tree, weighted K-nearest neighbor, and linear support vector machine. Furthermore, different combinations of proposed features were employed as a feature selection procedure based on classifier's performance using the aforementioned trained classifiers. Results: The results of our proposed method to diagnose MI utilizing all the proposed features with an LR classifier include 90.37%, 94.87%, and 86.44% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of 0.006. Conclusion: Our proposed classification-based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.


Keywords


Biological signal processing, classification, cross-validation, electrocardiography, feature selection, linear support vector machine, myocardial infarction, simple tree, weighted K-nearest neighbor

Full Text:

PDF

References


Cardiovascular Diseases (CVDs), Fact Sheet. World Health Organization, Media Centre; Updated May, 2017.

Mendis S, Thygesen K, Kuulasmaa K, Giampaoli S, Mähönen M, Ngu Blackett K, et al. World Health Organization definition of myocardial infarction: 2008-09 revision. Int J Epidemiol 2011;40:139-46.

Thaler M. The only EKG Book you'll Ever Need. Lippincott Williams & Wilkins (LWW); 2017.

Al Touma A, Tafreshi R, Khan M, editors. Detection of Cardiovascular Abnormalities Through 5-Lead System Algorithm. IEEE: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI); 2016.

Lu H, Ong K, Chia P, editors. An automated ECG classification system based on a neuro-fuzzy system. Comput Cardiol 2000;27:387-90.

de Bliek EC. ST elevation: Differential diagnosis and caveats. A comprehensive review to help distinguish ST elevation myocardial infarction from nonischemic etiologies of ST elevation. Turk J Emerg Med 2018;18:1-0.

Zhang X, Li R, Dai H, Liu Y, Zhou B, Wang Z. Localization of Myocardial Infarction with Multi-lead Bidirectional Gated Recurrent Unit Neural Network. IEEE Access; 2019.

Tripathy RK, Bhattacharyya A, Pachori RB. Localization of Myocardial Infarction from Multi-lead ECG signals using multiscale analysis and convolutional neural network. IEEE Sensors J 2019;19:11437-48.

Lines GT, de Oliveira BL, Skavhaug O, Maleckar MM. Simple t-wave metrics may better predict early ischemia as compared to st segment. IEEE Trans Biomed Eng 2016;64:1305-9.

Ansari S, Farzaneh N, Duda M, Horan K, Andersson HB, Goldberger ZD, et al. A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE Rev Biomed Eng 2017;10:264-98.

Gupta R, Kundu P, editors. Dissimilarity Factor Based Classification of Inferior Myocardial Infarction ECG. IEEE: 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI); 2016.

Carley SD. Beyond the 12 lead: Review of the use of additional leads for the early electrocardiographic diagnosis of acute myocardial infarction. Emerg Med (Fremantle) 2003;15:143-54.

Arif M, Malagore IA, Afsar FA. Detection and localization of myocardial infarction using K-nearest neighbor classifier. J Med Syst 2012;36:279-89.

Safdarian N, Dabanloo NJ, Attarodi G. A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal. J Biomed Sci Eng 2014;7:818.

Nidhyananthan SS, Saranya S, Kumari RS, editors. Myocardial Infarction Detection and Heart Patient Identity Verification. IEEE: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET); 2016.

Sharma LN, Tripathy RK, Dandapat S. Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 2015;62:1827-37.

Noorian A, Dabanloo NJ, Parvaneh S, editors. Wavelet based method for localization of myocardial infarction using the electrocardiogram. Comput Cardiol 2014;2014:645-8.

Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000;101:E215-20.

Samworth RJ. Optimal weighted nearest neighbour classifiers. Ann Statist 2012;40:2733-63.

Wu J, Bao Y, Chan SC, Wu H, Zhang L, Wei XG, editors. Myocardial Infarction Detection and Classification-A New Multi-Scale Deep Feature Learning Approach. IEEE: 2016 IEEE International Conference on Digital Signal Processing (DSP); 2016.

Kohavi R, editor. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Montreal, Canada: Ijcai; 1995.

Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Statist 1992;46:175-85.

Stone CJ. Consistent nonparametric regression. Ann Statist 1977;5:595-620.

Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: Data mining, inference and prediction. Mathem Intelligencer 2005;27:83-5.

Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. . London: Cambridge University Press; 2000.

Cortes C, Vapnik VN. Support vector networks. Machine Learning 1995;20:273-95.

Quinlan JR. Induction of decision trees. Machine Learning 1986;1:81-106.


Refbacks

  • There are currently no refbacks.