Classification of Arrhythmia from ECG Signals using MATLAB

  • Priyanka Mayapur
Keywords: Electrocardiogram (ECG), Lead-II Configuration, Cardiac Arrhythmia, Signal Processing, Matlab

Abstract

An Electrocardiogram (ECG) is defined as a test that is performed on the heart to detect any abnormalities in the cardiac cycle. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. The work proposed in this paper has been implemented using MATLAB. In this paper, we have proposed an efficient method to classify the ECG into normal and abnormal as well as classify the various abnormalities. To brief it, after the collection and filtering the ECG signal, morphological and dynamic features from the signal were obtained which was followed by two step classification method based on the traits and characteristic evaluation. ECG signals in this work are collected from MIT-BIH, AHA, ESC, UCI databases. In addition to this, this paper also provides a comparative study of various methods proposed via different techniques. The proposed technique used helped us process, analyze and classify the ECG signals with an accuracy of 97% and with good convenience.

References

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[5] Yan Sun, KapLuk Chan, & Shankar Muthu Krishnan. (2005). Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovascular Disorders. Available at: https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/1471-2261-5-28..
[6] Nitish V. Thakor & Yi-Sheng Zhu. (1991). Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection. IEEE Transactions on Biomedical Engineering, 18(8), 785-794.
[7] Hussain K. Khleaf, Kamarul Hawari Ghazali, & Ahmed N. Abdalla. (2013). Features extraction technique for ECG recording paper. Proceeding of the International Conference on Artificial Intelligence in Computer Science and ICT.
[8] Smita L. Kasar & Madhuri S. Joshi. (2016). Analysis of multi-lead ECG signals using decision tree algorithms. International Journal of Computer Applications, 134(16), 27-30.
[9] Anusha F.G & Jeba Sheela A. (2015). Automatic identification ECG anomalous using xml data processing. International Journal of Engineering Development and Research, 1-5. Available at: https://www.ijedr.org/papers/IJEDRCP1502025.pdf.
[10] Anuj Sharma & Prof. V.K. Joshi. 92015). Early diagnosis of heart defect using digital signal processing. International Journal of Electronics, Electrical and Computational System, 4(6), 27-31.
[1] A.Peterkova & M. Stremy. (2015). The raw ECG signal processing and the detection of QRS complex. 2015 IEE European Modelling Symposium, 80-85. Available at: http://uksim.info/ems2015/data/0206a080.pdf.
[2] Afseen Naaz & MrsShikha Singh. (2015). QRS complex detection and ST segmentation of ECG signal using wavelet transform. International Journal of Research in Advent Technology, 3(6), 45-50.
[3] Kritika Bawa & Pooja Sabherwal. (2014). R-peak detection by modified pan-tompkins algorithm. International Journal of Advancements in Research & Technology, 3(5), 30-33.
[4] A.Muthuchudar & Lt. Dr. S.SantoshBaboo. (2013). A study of the processes involved in ECG signal analysis. International Journal of Scientific and Research Publications, 3(3). Available at: http://www.ijsrp.org/research-paper-0313/ijsrp-p15114.pdf.
[5] Yan Sun, KapLuk Chan, & Shankar Muthu Krishnan. (2005). Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovascular Disorders. Available at: https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/1471-2261-5-28..
[6] Nitish V. Thakor & Yi-Sheng Zhu. (1991). Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection. IEEE Transactions on Biomedical Engineering, 18(8), 785-794.
[7] Hussain K. Khleaf, Kamarul Hawari Ghazali, & Ahmed N. Abdalla. (2013). Features extraction technique for ECG recording paper. Proceeding of the International Conference on Artificial Intelligence in Computer Science and ICT.
[8] Smita L. Kasar & Madhuri S. Joshi. (2016). Analysis of multi-lead ECG signals using decision tree algorithms. International Journal of Computer Applications, 134(16), 27-30.
[9] Anusha F.G & Jeba Sheela A. (2015). Automatic identification ECG anomalous using xml data processing. International Journal of Engineering Development and Research, 1-5. Available at: https://www.ijedr.org/papers/IJEDRCP1502025.pdf.
[10] Anuj Sharma & Prof. V.K. Joshi. 92015). Early diagnosis of heart defect using digital signal processing. International Journal of Electronics, Electrical and Computational System, 4(6), 27-31.
Published
2018-12-31
How to Cite
Priyanka Mayapur. (2018). Classification of Arrhythmia from ECG Signals using MATLAB. International Journal of Engineering and Management Research, 8(6), 115-129. https://doi.org/10.31033/ijemr.8.6.11