References
[1]. Hari Mohan Rai, and Anurag Trivedi, (2012).
“Classification of ECG Waveforms for Abnormalities
Detection using DWT and Back Propagation Algorithm”.
International Journal of Advanced Research in Computer
Engineering & Technology, Vol. 1, No. 4.
[2]. S. Osowski, and T.H. Linh, (2001). “ECG beat
recognition using fuzzy hybrid neural network”. IEEE Trans.
Biomed. Eng. Vol. 48, pp. 1265-1271.
[ 3 ] . Maedeh Kiani Sarkaleh and Asadollah
Shahbahrami, (2012). “Classification of ECG arrhythmias
using discrete wavelet transform and neural networks”.
IJCSEA, Vol. 2, No. 1.
[4]. K. Minami, H. Nakajima and T. Toyoshima, (1999).
“Real-Time discrimination of ventricular tachyarrhythmia
with fourier-transform neural network”. IEEE Trans. on
Biomed. Eng, Vol. 46, pp.179-185.
[5]. I. Romero and L. Serrano, (2001). “ECG frequency domain features extraction: A new characteristic for
rd arrhythmias classification”. in Proc. 23 Annual Int. Conf.
on Engineering in Medicine and Biology Society, pp.
2006-2008.
[6]. P. de Chazal, M. O'Dwyer and R. B. Reilly, (2000). “A
comparison of the ECG classification performance of
different feature sets”. IEEE Trans. on Biomed. Eng, Vol. 27,
pp. 327-330.
[7]. P. de Chazal, M. O'Dwyer and R. B. Reilly, (2004).
“Automatic classification of heartbeats using ECG
morphology and heartbeat interval features”. IEEE Trans.
on Biomed. Eng, Vol. 51, pp. 1196-1206.
[8]. C. Alexakis, H. O. Nyongesa, R. Saatchi, N. D. Harris,
C. Davis, C. Emery, R. H. Ireland and S. R. Heller, (2003).
“Feature extraction and classification of electrocardiogram
(ECG) signals related to hypoglycemia”. Proc. Computers
in Cardiology, Vol. 30, pp. 537-540.
[9]. P. Ivanov, M. QDY, R. Bartsch, et al, (2009). “Levels of
complexity in scaleinvariant neural signals”. Physical
Review.
[10]. N. Srinivasan, D.F. Ge, S.M. Krishnan, “Autoregressive
Modeling and Classification of Cardiac Arrhythmias”.
Proceedings of the Second Joint Conference, TX. USA.
[11]. Hafizah Hussain, Lai Len Fatt, (2007). “Efficient ECG
Signal Classification Using Sparsely Connected Radial
th Basis Function Neural Network”. Proceeding of the 6
WSEAS International Conference on Circuits, Systems,
Electronics, Control and Signal Processing, pp. 412-416.
[12]. Marcel R. Risk, Jamil F. Sobh, and J. Philip Saul,
(1997). “Beat Detection and Classification of ECG using
th Self Organizing Maps”. Proceedings of 19 International
Conference - IEEEIEMBS, Chicago, IL. USA.
[13]. Yuksel Ozbay, Rahime Ceylan, and Bekir Karlik,
(2011). “Integration of type-2 fuzzy clustering and wavelet
transform in a neural network based ECG classifier”. Expert
Systems with Applications, Vol. 38, pp. 1004-1010.
[14]. Physionet. The MIT-BIH Arrhythmia Database:
Retrieved from http://physionet.ph.biu.ac. il/
physiobank/database/mitdb/
[15]. R. Mark and G. Moody, MIT-BIH Arrhythmia Database Directory. Retrieved from http://ecg.mit.edu /dbinfo.html
[16]. Hari Mohan Rai, and Anurag Trivedi, (2012). “Denoising
of ECG waveforms using multiresolution wavelet
transform”. International Journal of Computer
Application, Vol. 45, No.18.
[17]. Michel Misiti, Yves Misiti, Georges Oppenheim, and
Jean-Michel Poggi, (1996). “Wavelet Toolbox for use with
MATLAB”. Vol. 1.
[18]. A. R. Sahab, and Y. Mehrzad Gilmalek, (2011). “An
Automatic Diagnostic Machine for ECG Arrhythmias
classification Based on Wavelet Transformation and
Neural Networks”. International Journal of Circuits,
Systems and Signal Processing, Vol. 5, No. 3.
[19]. Richard O. Dude, Peter E Hart David G Stork, (2002).
Pattern Classification: II Edition, John Wiley.
[20]. Mathworks. Neural Network Toolbox. Retrieved from
http:// www.mathworks.com
[21]. L. Khadra, A. Fraiwan, and W. Shahab, (2002).
“Neural-wavelet analysis of cardiac arrhythmias”.
Proceedings of the WSEAS International Conference on
Neural Network and Applications (NNA '02), Interlaken,
Switzerland, pp.3241-3244.
[22]. Qian Zheng, Chao Chen, and Zhinan Li, (2013). “A
Novel Multi-Resolution SVM (MR-SVM) Algorithm to detect
ECG signals anomaly”. in WE-CARE Project – Center for
Wireless Communication and Signal Processing.
[23]. Sarikal, P. and Wahidabanu, R., (2010). “Robust R
peak & QRS detection in electrocardiogram using
wavelet transform”. (IJACSA) International Journal of
Advanced Computer Science Applications, Vol.1(6), pp.
48-53.
[24]. Gothwal, H., Kedawat, S., & Kumar, R. (2011).
“Cardiac arrhythmias detection in an ECG beat signal
using fast Fourier transform and artificial neural network”.
Journal of Biomedical Science & Engineering, Vol. 4(4),
pp. 289-296.
[25]. Qibin Zhao and Liqing Zhan, (2005). “ECG Feature
Extraction and Classification Using Wavelet Transform and
Support Vector Machines”. International Conference on
Neural Networks and Brain, ICNN & B, Vol. 2, pp. 1089-1092.
[26]. Awadhesh Pachauri, and Manabendra Bhuyan,
(2009). “Robust Detection of R-Wave Using Wavelet
Technique”. World Academy of Science, Engineering
and Technology, Vol. 56.
[27]. Ashley EA, and Niebauer J., (2004). Conquering the
ECG. London: Remedica.
[28]. F.A Davis, (2005). ECG notes.
[29]. V. S. Chouhan, and S. S. Mehta, (2008). “Detection of
QRS Complexes in 12- lead ECG using Adaptive
Quantized Threshold”. IJCSNS International Journal of
Computer Science and Network Security, Vol. 8, No. 1.
[30]. M.B. Tayel, and Mohamed E. El-Bouridy, (2006).
“ECG Images Classification Using Feature Extraction
Based On Wavelet Transformation and Neural Network”.
ICGST, International Conference on AIML.
[31]. P. Tadejko, and W. Rakowski, (2007). “Mathematical
Morphology Based ECG Feature Extraction for the Purpose
th of Heartbeat Classification”. 6 International Conference
on Computer Information Systems and Industrial
Management Applications, CISIM '07, pp. 322-327.
[32]. F. Sufi, S. Mahmoud, and I. Khalil, (2008). “A new ECG
obfuscation method: A joint feature extraction &
corruption approach”, International Conference on
Information Technology and Applications in
Biomedicine, pp. 334-337.
[33]. S.C. Saxena, A. Sharma, and S.C. Chaudhary,
(1997). “Data compression and feature extraction of ECG
signals”. International Journal of Systems Science, Vol. 28,
No. 5, pp. 483-498.
[34]. Emran M. Tamil, Nor Hafeezah Kamarudin, Rosli
Salleh, M. Yamani Idna Idris, Noorzaily M. Noor, and Azmi
Mohd Tamil, (2008). “Heartbeat Electrocardiogram (ECG)
Signal Feature Extraction Using Discrete Wavelet
Transforms (DWT)”.
[35]. E.D. Übeyli, (2009). “Detecting variabilities of ECG
Signals by Lyapunov Exponents”. Neural Computing and
Applications, Vol.18, No. 7, pp. 653-662.
[36]. Alan Jovic, and Nikola Bogunovic, (2007). “Feature
Extraction for ECG Time- Series Mining based on Chaos Theory”. Proceedings of 29 International Conference on Information Technology Interfaces.