References
- Pawar T., Chaudhuri S., and Duttagupta S.P., 2007, Body movement activity recognition for ambulatory cardiac monitoring. In IEEE Transaction on Biomedical Engineering, 54(5), 874–882
- Pawar, T., Anantakrishnan, N.S., Chaudhuri, S., and Duttagupta S.P., 23–26 August 2007, Impact Analysis of Body Movement in Ambulatory ECG, Proceedings of the 29th Annual International, Conference of the IEEE EMBS, Cité Internationale, Lyon, France, 23–26 August 2007
- Chaudhuri S., Pawar T., and Siddhartha S.P., 2009, Ambulation analysis in wearable ECG (New York, USA: Springer) ISBN: 978-1-4419-0723-3
- Deepak Vala, Pawar T., 2012, A survey on ambulatory ECG and identification of motion artifacts, International Journal of Engineering Research and Development, 1(7), 38–41
- Nimbargi V.S., Gadre V.M., and Mukherji S., 2005, Characterization of motion artifacts using wavelet transform and neural networks, Indian Conference on Medical Informatics and Telemedicine, Kharagpur, West Bengal, India 2005
- Elbuni, A., Kanoun, S., Dr.Elbuni, M., and Dr. Ali, N., December 2009, ECG parameter extraction algorithm using DWTAE algorithm. International Conference on Computer Technology and Development (Cairo, Egypt), December 2009, pp. 315–320
- Dhubkarya D.C., Aastha Katara, and Raj Kumar Thenua. 2012, Simulation of Adaptive Noise Canceller for an ECG signal Analysis. ACEEE International Journal on Signal & Image Processing, 3(2), 1–4
- Akshay, N., Naga Jonnabhotla, N.A.V., Sadam, N., and Yeddanapudi, N.D., September 2010, ECG Noise Removal and QRS Complex Detection Using UWT, International Conference on Electronics and formation Engineering (Kyoto), September 2010, pp. V2-438–V2-442
- Zia Ur Rahman, M., Ahamed Shaik, R., and Rama Koti Reddy D.V., December 2009, Noise cancellation in ECG signals using normalized sign-sign LMS algorithm. International Symposium on Signal Processing and Information Technology in Ajman (United Arab Emirates), December 2009, pp. 288–292
- Zia Ur Rahman, M., Ahamed Shaik, R., and Rama Koti Reddy, D.V., January 2009, Adaptive noise removal in the ECG using the Block LMS Algorithm. 2nd International Conference on Adaptive Science and Technology (Accra), January 2009, pp. 380–383
- Rahul kher, Dipak Vala, Pawar, T., and Thakar V.K., December 2009, Introduction to ambulation study of ECG in wearable devices. International Conference on Signals, Systems & Automation (ICSSA-2009), (GCET V.V. Nagar), December 2009 pp. 344–348
- Pawar, T., Chaudhuri, S., and Duttagupta, S.P., August 2006, Analysis of ambulatory ECG signal, 28th International Conference of the IEEE on Engineering in Medicine (New York), August 2006, pp. 3094–3097
- 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, Journal of Electrocardiology, 26, 66–73
- http://www.biopac.com/productimages/MP36%20Physiology%20System.jpg
- Haykin S., 2001, Neural Networks: A Comprehensive Foundation (Delhi, India: Pearson Education Asia)
- Vijaya G., Kumar V. and Verma H.K., 1998. ANN-based QRS-complex Analysis of ECG. Journal of Medical Engineering & Technology, 22(4), 160–167
- Özbay, Y., and Karlik, B., 25–28 October 2001, A Recognition of ECG Arrhythmias using Artificial Neural Networks, Proceedings of the 23rd Annual EMBS International Conference (Istanbul, Turkey), 25–28 October 2001, pp. 1680–1683
- Liang-Yu Shyu, Ying-Hsuan Wu, and Weichih Hu, 2004, Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG. IEEE Transactions on Biomedical Engineering, 51(7), 1269–1273
- Rosaria Silipo and Carlo Marchesi, 1998, Artificial Neural Networks for Automatic ECG Analysis. IEEE Transactions on Signal Processing, 46(5), 1417–1425
- Prasad G.K. and Sahambi J.S., 15–17 October 2003, Classification of ECG arrhythmias using multi-resolution analysis and neural networks, Conference on Convergent Technologies for the Asia-Pacific Region, 15–17 October 2003, pp. 227–231
- İnan Güler and Elif Derya Übeyl, 2005, ECG beat classifier designed by combined neural network model. Pattern Recognition, 38(2), 199–208