Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network

  • Authors

    • Amerah Hanis Hussin
    • Ahmad Syukri Abdul Aziz
    • Megat Syahirul Amin Megat Ali
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.11.20814
  • Myocardial Infarction, ECG, Power Ratio, k-Nearest Neighbor, Artificial Neural Network.

  • Abstract

    Myocardial infarction is an irreversible damage of heart muscle caused by prolonged oxygen deficiency. As a result, the presence of damaged tissue will alter the normal sinus rhythm. Hence, the paper proposes to profile history of myocardial infarction from electrocardiogram using artificial neural network. Data for anterior and inferior myocardial infarction, as well as healthy control is acquired from PTB Diagnostic ECG Database. Subsequently, QRS power ratio features for different frequency zones are extracted from the pre-processed electrocardiogram. Discriminative ability of the features is assessed using k-nearest neighbor. The best combination of features with 99.7% testing accuracy is the power ratio composite that combines both low-frequency and mid-frequency information. An intelligent profiling model is successfully developed using the composite features and an optimized artificial neural network. The model was able to identify between different electrocardiogram groups with overall accuracy of 98.4% and mean squared error of less than 0.1. Conclusively, the proposed signal processing approach has provided an improved alternative to the established methods from literature.

     

     

  • References

    1. [1] Thygesen K, Alpert JS & White HD (2007), Universal definition of myocardial infarction. European Heart Journal 28, 2525–2538.

      [2] Inan OT, Giovangrandi L & Kovacs GTA (2006), Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering 53, 2507–2515.

      [3] Megat Ali MSA, Jahidin AH, Norali AN & Mat Som MH (2011), Classification of bundle branch blocks using multilayered perceptron network. Proceedings of IEEE International Conference on Control System, Computing and Engineering, pp. 531–535.

      [4] Ahmad Shukri MH, Megat Ali MSA, Noor MZH, Jahidin AH, Saaid MF & Zolkapli M (2012), Investigation on Elman neural network for detection of cardiomyopathy. Proceedings of IEEE Control and System Graduate Research Colloquium, pp. 328–332.

      [5] Tsutsumi T, Okamoto Y, Kubota-Takano N, Wakatsuki D, Suzuki H, Sezaki K, Iwasawa K & Nakajima T (2014), Time-frequency analysis of the QRS complex in patients with ischemic cardiomyopathy and myocardial infarction. IJC Heart and Vessels 4, 177–187.

      [6] Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM & Pajak A (1994), Myocardial infarction and coronary deaths in the World Health Organization MONICA Project, Circulation 90, 583–612.

      [7] Megat Ali MSA, Jahidin AH, Yassin IM, Pasya I, Abdul Khalid MF & Awang Z (2017), Characterization of post-MI electrocardiogram using power ratio features and k-nearest neighbor classifier. Journal of Fundamental and Applied Sciences 9, 937–951.

      [8] Megat Ali MSA, Jahidin AH, Md Tahir N & Taib MN (2014), Learning style classification via EEG sub-band spectral centroid frequency features. International Journal of Electrical and Computer Engineering 4, 931–938.

      [9] Megat Ali MSA, Che Zainal CZA, Husman A, Saaid MF, Noor MZH & Jahidin AH (2012), Detection of cardiomyopathy using multilayered perceptron network. Proceedings of IEEE 8th International Colloquium on Signal Processing and its Applications, pp. 436–440.

      [10] Guraliuc AR, Barsocchi P, Potorti F & Nepa P (2011), Limb movements classification using wearable wireless transceivers. IEEE Transactions on Information Technology in Biomedicine 15, 474–480.

      [11] Haron MH, Taib MN, Megat Ali MSA & Mohd Yunus M (2012), Gender classification based on human radiation frequencies of chakra points and brain regions. Proceedings of IEEE International Conference on Electronics Design, Systems and Applications, pp. 188–193.

      [12] Jahidin AH, Taib MN, Md Tahir N, Megat Ali MSA, Yassin IM, Lias S, Mohd Isa R, Omar WRW & Fuad N (2013). Classification of intelligence quotient using EEG sub-band power ratio and ANN during mental task. Proceedings of IEEE Conference on Systems, Process and Control, pp. 204–208.

      [13] Amiruddin AI, Megat Ali MSA, Saaid MF, Jahidin AH & Noor MZH (2013), Feature reduction and arrhythmia classification via hybrid multilayered perceptron network. Proceedings of IEEE 3rd International Conference on System Engineering and Technology, pp. 290–294.

      [14] Lin C-H (2008), Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Computers and Mathematics with Applications 55, 680–690.

      [15] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GBPeng C-K & Stanley HE (2000), PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101, e215–e220.

      [16] Charalambous C (1985), The performance of an algorithm for minimax design of two-dimensional linear phase FIR digital filters. IEEE Transactions on Circuits and Systems 32, 1016–1028.

      [17] Kohavi R (1995), A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of International Joint Conference on Artificial Intelligence, pp. 1137–1145.

      [18] Islam MM & Murase K (2001), A new algorithm to design compact two-hidden-layer artificial neural networks. Neural Network 14, 1265–1278.

      [19] Xu S & Chen L (2008), A novel approach for determining the optimal number of hidden layer neurons for FNN’s and its application in data mining. Proceedings of 5th International Conference on Information Technology and Applications, pp. 683–686.

      [20] Benardos PG & Voniakos G-C (2007), Optimizing feedforward artificial neural network architecture. Engineering Applications of Artificial Intelligence 20, 365–382.

  • Downloads

  • How to Cite

    Hanis Hussin, A., Syukri Abdul Aziz, A., & Syahirul Amin Megat Ali, M. (2018). Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network. International Journal of Engineering & Technology, 7(4.11), 236-240. https://doi.org/10.14419/ijet.v7i4.11.20814

    Received date: 2018-10-03

    Accepted date: 2018-10-03

    Published date: 2018-10-02