Abstract

The abnormal condition of the electrical activity in the heart is using electrocardiogram shows a threat to human beings. It is a representative signal containing information about the condition of the heart. The P-QRS-T wave shape, size and their time intervals between its various peaks contain useful information about the nature of disease affecting the heart. This paper presents a technique to examine electrocardiogram (ECG) signal, by taking the features form the heart beats classification. ECG Signals are collected from MIT-BIH database. The heart rate is used as the base signal from which certain parameters are extracted and presented to the network for classification. This survey provides a comprehensive overview for the classification of heart rate.

References

• Leif Sornmo, Pablo Laguna. , Electrocardiogram (ECG) Signal Processing".
• Pan, J. and Tompkins, W. J. 1985 A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng. , vol. 32, pp. 230–236.
• Afonso, V. X. , Tompkins, W. J. , Nguyen, T. Q and S. Luo. 1999. ECG beat detection using filter banks, IEEE Trans. Biomed. Eng. , vol. 46, pp. 192–202.
• Bahoura, M. , Hassani, M. and Hubin, M. 1997. DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis, Comput. Methods Programs Biomed, vol. 52, no. 1, pp. 35–44.
• Hu, Y. H. , Tompkins, W. J. , Urrusti, J. L and Afonso, V. X. 1993. Applications of artificial neural networks for ECG signal detection and classification, J . Electro cardiology, vol. 26 (Suppl. ), pp. 66-73.
• Xiaomin, Xu. and Ying, Liu. 2004. ECG QRS Complex Detection Using Slope Vector Waveform (SVW) Algorithm, Proceedings of the26th Annual International Conference of the IEEE EMBS, pp. 3597-3600.
• Okada, M. 1979. A digital filter for the QRS complex detection, IEEE Trans. Biomed. Eng. , Vol. BME-26, pp. 702-703.
• Afonso, V. X. and Tompkins, W. J. 1999. ECG Beat Detection Using Filter Banks, IEEE Trans. Biomed. Eng. , Vol. 46, No. 2, pp. 192-202.
• Kohler, BU. , Henning, C and Orglmeister, R. 2002. The principles of software QRS detection, IEEE Eng. Med. Biol. Mag. , vol. 21, no. 1, pp. 42–57.
• Dinh, HAN. , Kumar, DK. , Pah, ND and Burton, P. 2001. Wavelets for QRS detection, in Proc. 23rd IEEE EMBS Int. Conf, pp. 1883– 1887.
• Laguna, P. , Jane, R and Caminal, P. 1994. Automatic detection of wave ´boundaries in multi lead ECG signals: Validation with the CSE database, Comput. Biomed. Res. , vol. 27, no. 1, pp. 45–60.
• Martinez, J. P. , Almeida, R. , Olmos, S. , Rocha, AP and Laguna, P. 2004. A wavelet-based ECG delineator: Evaluation on standard databases, IEEE Trans. Biomed. Eng. , vol. 51, no. 4, pp. 570–581.
• Chouhan, V. S and Mehta, S. S. 2008. Threshold-based detection of P and T-wave in ECG using new feature signal, Int. J. Comp. Science Net. Security, vol. 8, no. 2, pp. 144–152.
• Martinez, J. P and Olmos, S. 2004. Methodological principles of T wave alternans analysis: A uni?ed framework, IEEE Trans. Biomed. Eng. , vol. 52, no. 4, pp. 599–613.
• Cuiwei Li. , Chongxun Zheng and Changfeng Tai. 1995. Detection of ECG Characteristic points using Wavelet Transforms, IEEE Trans. Biomed. Eng, Vol. 42, No. 1.
• Mahmoodabadi, S. Z. , Ahmadian, A. , Abolhasani, M. D. , Eslami, M and Bidgoli, J. H. 2005. ECG Feature Extraction Based on Multiresolution Wavelet Transform, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4.
• Saxena, SC. , Kumar, V and Hande, ST. 2002. QRS Detection using New Wavelets, Journal of Medical Engineering & Technology, Volume 26, November1, pages 7-15.
• Chouhan, V. S and Mehta, S. S. 2008. Detection QRS Complex in 12 lead ECG using Adaptive Quantized Threshold, International Journal of Computer Science and Network Security, Vol 8 No. 1.
• Chouhan, V. S. , Mehta, SS and Ligayat, N. S. 2008. Delineation QRS Complex, P and T wave in 12 Lead ECG"IJCSNS Vol. 8 No. 4.
• Kyrkos, A. , Giakoumakis, E. A and Carayannis, G. 1998. QRS Detection through Time Recursive Prediction Technique, Signal Processing 15(1988) 429-436.
• Pan, J and Tompkins, W. 1985. A Real Time QRS Detection Algorithm, IEEE Transaction sons on Biomedical Engineering, vol. 32, no. 3, pp. 230- 236.
• Schreicr, G. , Kastner, P. and Marko, W. 2001. An Automatic ECG Processing Algorithm to Identify Patients Prone to Paroxysmal Atrial Fibrillation, IEEE Computers in Cardlology, vol. 28, pp. 133- 135.
• Jaipupan and Tompkins, W. 1985. A Real-Time QRS Detection Algorithm, IEEE Transaction on Biomedical Engineering, Vol, Bme-32, No3.
• Li, C. W. , Zheng, C. X and Tai, C. F. 1995. Detection of ECG characteristic points using wavelet transforms, IEEE Trans. Biomed. Eng. 42 (1) (1995), pp 21–28, (1995)
• Awadhesh Pachauri and Manabendra Bhuyan. , Robust Detection of R-Wave Using Wavelet Technique, World Academy of science, Engineering and technology 56-(2009)
• Yeh, Y. C and Wang, W. J. 2008. QRS Complexes detection for ECG signal: The Difference Operation Method", Elsevier Journal, Computer Methods and Programs in Biomedicine, 245-254, (2008)
• Rajendra Acharya, U. , Subbanna Bhat, P. , Iyengar, S. S. 2003. Ashok Rao and Sumeet Dua. , Classification of heart rate data using artificial neural network and fuzzy equivalence relation", Pattern Recognition 36 (2003) 61 – 68.
• Alexakis, C. , Nyongesa, HO. , Saatchi, R. , Harris, ND. , Davies, C. , Emery, C. , Ireland, RH and Heller SR. 2003. Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycemia", Conference on computers in Cardiology, pp. 537-540, IEEE.
• Silipo, R and Marchesi, C. 1998. Artificial neural networks for automatic ECG analysis, Signal Processing 1998. 46; 1417-1425.
• Papaloukas, C and Fotiadis, D. I. 2002. An ischemia detection method based on artificial neural network, Artificial Intelligence in Medicine 2002; 24: 167-178.
• Foo, SY and Stuart, G. 2002. Neural network-based ECG pattern recognition, Engineering Applications of Artificial Intelligence 2002; 15: 253-260.
• Ceylan, R and Ozbay, Y. 2007. Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network, Expert Systems with Applications, 286-295.
• Kutlu, Y and Kuntalp, D 2008. Arrhythmia classification using higher order statistics, IEEE Signal Processing, Communication and Applications Conference 2008; IEEE Press: 1-4.
• Cvikl, M and Zemva, A. 2010. FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm", Digital Signal Processing 2010; 20: 238–248, (2010)
• Tadejko, P and Rakowski, W. 2007. Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification, 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM '07, pp. 322-327
• Tayel, M. B and El-Bouridy, M. E. 2006. ECG Images Classification Using Feature Extraction Based On Wavelet Transformation And Neural Network, ICGST, International Conference on AIML.
• Tareen, S. G. 2008. Removal of Power Line Interference and other Single Frequency Tones from Signals, M. Sc, Computer Science and Electronics, Mälardalen University, sweedan.
• El-Dahshan, ESA. 2010. Genetic algorithm and wavelet hybrid scheme for ECG signal denoising, Telecommunication Systems, vol. 46, pp. 209-215.
• Mbachu, C. B. , Onoh, G. N. , Idigo V. E. , Ifeagwu E. N and Nnebe S. U. 2011. Processing ECG Signal with Kaiser Window- based FIR Digital Filters" International Journal of Engineering Science and Technology, Vol. 3 No. 8, 6775-6783.
• Dutta, SSDM. 2011. Optimized Noise Canceller for ECG Signals, International Conference on Intelligent Systems and Data Processing (ICISD).
• Chang, K. M. 2010. Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition, Sensors, 2010, doi:10. 3390/s100606063
• Kaur, M. , Singh, B. , Ubhi, JS and Seema Rani. 2011. Digital Filteration of ECG Signals for Removal of Baseline Drift", 2011 International Conference on Telecommunication Technology and Applications Proc . of CSIT vol. 5 pg no. 105-109.
• Seema Rani, Amarpreet kaur and Ubhi, J. S. 2011. Comparative study of FIR and IIR filters for the removal of Baseline noises from ECG signal, International Journal of Computer Science and Information Technologies Vol 2 (3).
• Harting, L. P. , Fedotov, N. M and Slump, C. S. 2004. On Baseline Drift Suppressing in ECG Recording" 2004 IEEE Benelux Signal Processing Symposium.
• Karnewar, J. S and Sarode, M. V. 2013. The Combined Effect of Median and FIR Filter in Pre-processing of ECG Signal using Matlab, International Journal of Computer Applications (0975 – 8887), National Level Technical Conference "X-PLORE 13.
• Jane, R. , Laguna, P. , Thakor, N. V and Caminal, P. 1992. Adaptive Baseline Wander Removal in the ECG: Comparative Analysis with Cubic Spline Technique" IEEE proceeding Computers in Cardiology, pp. 143- 146.
• Priyanka Mehta and Monika Kumari. 2012. QRS Complex Detection of ECG Signal Using Wavelet Transform", International Journal of Applied Engineering Research, Vol. 7 No. 11.
• Ros, E. , Mota, S. , Fernandez, F. , Toro, F and Bernier, J. 2004. ECG characterization of paroxysmal Atrial ?brillation: Parameter extraction and automatic diagnosis algorithm, Computers in Biology and Medicine, vol. 34, no. 8, pp. 679 – 696.
• Hickey, B. , Heneghan, C and Chazal, P. D. 2004. Non-episode-dependent assessment of paroxysmal atrial ?brillation through measurement of RR interval dynamics and atrial premature contractions, Annals of Biomedical Engineering, vol. 32, no. 5, pp. 677 – 687.
• Rajendra Acharya, U. , Subbanna Bhat, P. , Iyengarc, SS. , Ashok Rao, Sumeet Dua. 2002. Classification of heart rate data using artificial neural network and fuzzy equivalence relation", Pattern Recognition Society.