Automatic detection and localization of myocardial infarction using back propagation neural networks

2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010

Volumn , Issue , 2010, Pages

  Muhammad, Arif ^a   Malagore, Ijaz A ^b   Afsar, Fayyaz A ^b  

^a AIR UNIVERSITY   (Pakistan)

^b PAKISTAN INSTITUTE OF ENGINEERING AND APPLIED SCIENCES   (Pakistan)

Author keywords

Backpropagation neural networks; Component; ECG; Myocadial infarction; Principal component analysis

Indexed keywords

AUTOMATIC DETECTION; BACK PROPAGATION NEURAL NETWORKS; BEAT CLASSIFICATION; COMPONENT; ECG; ECG SIGNALS; MYOCADIAL INFARCTION; MYOCARDIAL INFARCTION; PRINCIPAL COMPONENTS; PRINCIPAL COMPONENTS ANALYSIS; Q WAVES; SENSITIVITY AND SPECIFICITY; T WAVES; TIME DOMAIN;

AUDIO SYSTEMS; BACKPROPAGATION; BIOINFORMATICS; CHEMICAL DETECTION; CLASSIFIERS; ELECTROCARDIOGRAPHY; ELECTROCHROMIC DEVICES; LINEAR TRANSFORMATIONS; NEURAL NETWORKS; TIME DOMAIN ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77956134264     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICBBE.2010.5514664     Document Type: Conference Paper

References (13)

1. Reddy MRSE, L. Svensson, J. Haisty and W.K. Pahlm, "Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG", In Computers in Cardiology, 1992, 667-670.
2. HL Lu KO, P Chia, "An Automated. ECG Classification System Based on a Neuro-Fuzzy System", In Computers in Cardiology, 2000, pp. 387-390.
3. Goldberg AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research. Resource for Complex Physiologic Signals. Circulation, 101(23), 2000, pp. e215-e220.
4. Martinez, J.P., et al, "A wavelet-based ECG delineator: evaluation on standard databases", IEEE Transactions on Biomedical Engineering, 51(4), 2004, pp. 570-581.
5. Afsar Minhas, F.A. and M. Arif, "QRS Detection and Delineation Techniques for ECG Based Robust Clinical Decision Support System. Design", Proc of National Science Conference. 2007.
6. Leif Sornmo and Pablo Laguna, "Bio electrical signal processing in cardiac and neurological applications", Academic Press, 2005.
7. K. Daqourq, "ECG baseline wanders reduction using DWT", Asian journal of information technology 4(11), 2005, pp. 989-995.
8. Gari D.Clifford, "Advanced methods and tools for ECG data analysis", Artech-House INC, 2006.
9. Pearson, K., "On Lines and Planes of Closest Fit to Systems of Points in Space" Philosophical Magazine 2 (6), 1901, pp. 559-572.
10. Reddy MRSE, L. Svensson, J. Haisty and W.K. Pahlm, "Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG", In Computers in Cardiology, ,1992, pp. 667-670.
11. HL Lu KO, P Chia, "An Automated ECG Classification System Based on a Neuro-Fuzzy System", In Computers in Cardiology, 2000. Pp. 387-390.
12. Bo Heden, Hans Öhlin, Ralf Rittner and Lars Edenbrandt, "Acute Myocardial Infarction Detected in the 12-Lead ECG by Artificial Neural Networks", Circulation, 96, 1997, pp. 1798-1802.
13. Riedmiller, M. and Braun, H. (1993) A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In: Ruspini, H., (Ed.) Proc. Of the ICNN 93, San Francisco, 1993, pp. 586-591.