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ECG based Atrial Fibrillation Detection using Cuckoo Search Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 162 - Number 10 |
| Year of Publication: 2017 |
| Authors: Padmavathi Kora, V. Ayyem Pillai |
10.5120/ijca2017913371
|
Padmavathi Kora, V. Ayyem Pillai . ECG based Atrial Fibrillation Detection using Cuckoo Search Algorithm. International Journal of Computer Applications. 162, 10 ( Mar 2017), 37-42. DOI=10.5120/ijca2017913371
Abstract
Electrocardiogram (ECG), a non-invasive diagnostic technique, is used for detecting cardiac arrhythmia. For the last decade industry is dealing with Bio- medical instrumentation and research, demanding an advancement in its ability to distinguish different cardiac arrhythmia. Atrial Fibrillation (AF) is an irregular rhythm of the human heart. During AF, the atrial movements are quicker than the normal rate. As blood is not completely ejected out of atria, chances for the formation of blood clots in atrium. These abnormalities in the heart can be identified by the changes in the morphology of the ECG. The first step in the detection of AF is preprocessing of ECG, which removes noise using filters. Recent feature extraction methods, such as Auto Regressive (AR) modeling, Magnitude Squared Coherence (MSC) and Wavelet Coherence (WTC) using standard database (MIT-BIH), yielded a lot of features. Many of these features might be insignificant containing some redundant and non-discriminatory features that introduce computational burden and loss of performance. So Cuckoo Search Algorithm (CSA) is directly used to optimize the raw ECG instead of extracting features using the above feature extraction techniques. This paper proposes the design of an efficient system for classification cardiac arrhythmia such as ANN (Artificial Neural Network), KNN (K- Nearest Neighbor), SVM (State Vector Machine). Our simulation results show that CSA with ANN gives 99.3% accuracy on MIT-BIH database by including NSR database also.
References
- K. Padmavathi and K. Ramakrishna, “Classification of ecg signal during atrial fibrillation using burg’s method,” International Journal of Electrical and Computer Engineering, vol. 5, no. 1, p. 64, 2015.
- K. Padmavathi and K. Krishna, “Myocardial infarction detection using magnitude squared coherence and support vector machine,” in Medical Imaging, m-Health and Emerging Com- munication Systems (MedCom), 2014 International Conference on. IEEE, 2014, pp. 382– 385.
- G. B. Moody and R. G. Mark, “A new method for detecting atrial fibrillation using rr intervals,” Computers in Cardiology, vol. 10, no. 1, pp. 227–230, 1983.
- K. Tateno and L. Glass, “A method for detection of atrial fibrillation using rr intervals,” in Computers in Cardiology 2000. IEEE, 2000, pp. 391–394.
- M. Mohebbi and H. Ghassemian, “Detection of atrial fibrillation episodes using svm,” in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. IEEE, 2008, pp. 177–180.
- S. Karpagachelvi, M. Arthanari, and M. Sivakumar, “Ecg feature extraction techniques-a survey approach,” arXiv preprint arXiv:1005.0957, 2010.
- M. Effros, H. Feng, and K. Zeger, “Suboptimality of the karhunen-loeve transform for trans- form coding,” Information Theory, IEEE Transactions on, vol. 50, no. 8, pp. 1605–1619, 2004.
- R. J. Martis, U. R. Acharya, and L. C. Min, “Ecg beat classification using pca, lda, ica and discrete wavelet transform,” Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437–448, 2013.
- K. G. Beauchamp, Applications of Walsh and related functions. Academic press, 1984.H. Khorrami and M. Moavenian, “A comparative study of dwt, cwt and dct transformations in ecg arrhythmias classification,” Expert systems with Applications, vol. 37, no. 8, pp. 5751– 5757, 2010.
- R. Clayton and A. Murray, “Estimation of the ecg signal spectrum during ventricular fib- rillation using the fast fourier transform and maximum entropy methods,” in Computers in Cardiology 1993, Proceedings. IEEE, 1993, pp. 867–870.
- C. Saritha, V. Sukanya, and Y. N. Murthy, “Ecg signal analysis using wavelet transforms,” Bulg. J. Phys, vol. 35, no. 1, pp. 68–77, 2008.
- H. Krim and D. H. Brooks, “Feature-based segmentation of ecg signals,” in Time-Frequency and Time-Scale Analysis, 1996., Proceedings of the IEEE-SP International Symposium on. IEEE, 1996, pp. 97–100.
- S. Hargittai, “Savitzky-golay least-squares polynomial filters in ecg signal processing,” in Computers in Cardiology, 2005. IEEE, 2005, pp. 763–766.
- A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and phy- sionet components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
- S. Parvaresh and A. Ayatollahi, “Automatic atrial fibrillation detection using autoregressive modeling,” in 2011 International Conference on Biomedical Engineering and Technology, 2011, pp. 4–5.
- J. Lee, B. A. Reyes, D. D. McManus, O. Mathias, and K. H. Chon, “Atrial fibrillationdetection using an iphone 4s,” Biomedical Engineering, IEEE Transactions on, vol. 60, no. 1, pp. 203–206, 2013.
- X. Zhou, H. Ding, B. Ung, E. Pickwell-MacPherson, Y. Zhang et al., “Automatic online detection of atrial fibrillation based on symbolic dynamics and shannon entropy,” Biomed Eng Online, vol. 13, no. 18, pp. 1–18, 2014.
- K Padmavathi and K. S. Ramakrishna, “Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence,” International Journal of Systems, Control and Communications, vol. 6, no. 4, pp. 292–304, 2015.
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