Biologically inspired evolutionary computing tools for the extraction of fetal electrocardiogram

WSEAS Transactions on Signal Processing

Volumn 5, Issue 3, 2009, Pages 106-115

  Jatoth, Ravi Kumar ^a   Anoop, Saladi S V K K ^a   Prabhu, Ch Midhun ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Adaptive noise cancellation; Fetal ECG; Genetic algorithm (GA); Least mean squares(LMS); Particle swarm optimization (PSO)

Indexed keywords

ADAPTIVE NOISE CANCELLATION; ADAPTIVE NOISE CANCELLATIONS; BIOLOGICAL ACTIVITIES; BIOLOGICALLY INSPIRED; EVOLUTIONARY COMPUTING; FETAL ECG; FETAL ELECTROCARDIOGRAMS; FETAL HEART; FETAL HEART RATE EXTRACTION; HUMAN BODIES; INTRINSIC NOISE; LEAST MEAN SQUARES(LMS); LOW SIGNAL-TO-NOISE RATIO; MEDICAL DIAGNOSIS; NOISE CANCELLATION; QRS COMPLEXES; SKIN CONTACT; SPECTRAL PROPERTIES;

ADAPTIVE FILTERING; ADAPTIVE FILTERS; BIOLOGICAL ORGANS; ELECTRIC FILTERS; ELECTROCARDIOGRAPHY; ELECTROCHROMIC DEVICES; GENETIC ALGORITHMS; SIGNAL TO NOISE RATIO;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 70349641163     PISSN: 17905022     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. Amit Kumar, Lillie Dewan, Mukhtiar Singh. Real Time Monitoring System for ECG Signal Using Virtual Instrumentation,. WSEAS Transactions on Biology and Biomedicine Issue 11, Volume 3, 2006
2. Ch. Renumadhavi, S. Madhava Kumar, A. G. Ananth, Nirupama Srinivasan Algorithms for Filtering and Finding SNR of ECG Signals with Powerline Interference, WSEAS Transactions on Signal Processing Issue 9, Volume 2, September 2006 pp 1320-1325
3. B.W. Widrow, S.D. Stearns, " Adaptive signal processing" , 1985,Prentice-Hall, Inc., New Jearsy.
4. Levkov C, Michov G, Ivanov R, Daskalov IK. " Subtraction of 50 Hz interference from the elect rocardiogram" . Med & Biol, Eng & Comput 1984.
5. M B I Reaz,L S Weis " Adaptive linear network filter for fetal ECG extraction" 2004
6. " Comparision between blind seperation and adaptive noise cancellation techniques for fetal ECG extraction" IEE Savoy place London 1999
7. Haykin S. " Adaptive Filter Theory" . 3rd Edition. Prentice Hall 2002 ..
8. A. van Eck, " Abdominale foetale ECG opname" in Dutch), Twente Univ. Technol., Enschede, The Netherlands, Rep. 1224.0093, 1976.
9. T. Wheeler, A. Murrills, and T. Shelly, " Measurement of the fetal heart rate during pregnancy by a new electrocardiographic technique" Brit. J. Obstet. Gynecol., vol. 85, pp. 12-17, 1978
10. J.H.van Bemmel, " Detection of weak fetal electrocardiograms by autocorrelation and crosscorrelation of envelopes" IEEE Trans. Biomed. Eng., vol. BME-15, no. 1, pp. 17-23, 1968.
11. Bobbie, P. O., Chaudhari, H., Arif, C.-Z., and Pujari, S., Electrocardiogram (EKG) Data Acquisition and Wireless Transmission, WSEAS Transactions on Computers, vol. 3, issue 8, 2004, pp 2665 - 2672
12. L. El Khansa and A. Naït-Ali, Parametrical modelling of a premature ventricular contraction ECG beat: comparaison with the normal case, Computers in Biology and Medicine, vol. 37, 2007, pp. 1-7.
13. G. Mandellos, M. Koukias, G. Anastassopoulos, D. Lymperopoulos, A new SCP-ECG module for telemedicine services, WSEAS Transactions on Computers, November 2004, vol. 3, Iss. 5, pp. 1258- 1263.
14. P. Baldi and S. Brunak, " Bioinformatics," The Machine Learning Approach, MIT Press, London, UK, 2000.
15. McSharry P E, Clifford G D, Tarassenko L, Smith L A., A dynamic model for generating synthetic electrocardiogram signals, IEEE Trans. on Biomedical Engineering vol.50, 2003, pp.289-294
16. Zhao Z D and Chen Y Q., A new method for removal of baseline wander and power line interface in ECG signals, Proc. of the fifth Int. Conf. on Machine Learning and Cybernetics (Dalian) 13-16 aug. 2006, pp. 4342-4347 347, pp 12-15, 1996.
17. Selker, H. P., Griffith, J. L., Patil, S., Long, W. J., & D'Agostino, R. B. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. J. Investig. Med., No 43, Vol. 5. pp 468-476, 1995.
18. Polak, M. J., Zhou, S. H., Rautaharju, P. M., Armstrong, W. W., & Chaitman, B. R. Using automated analysis of the resting twelve-lead ECG to identify patients at risk if developing transient myocardial ischemia-an application of an adaptive logic network. Physiology. Meas., No 18 Vol. 4, pp 317-32, 19975.
19. Henrik Haraldsson, Lars Edenbrandt, Mattias Ohlsson, Detecting acute myocardialinfarction in the 12-lead ECG using Hermite expansions and neural networks, Artificial Intelligence in Medicine 2004 pp 127-135