This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. All these waves are represented respective heart functions. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. These features will then be fed as an input to neural network system. The target output represented real peaks of the signals is also being defined using a binary number. Result obtained showing that neural network pattern recognition is able to classify and recognize the real peaks accordingly with overall accuracy of 81.6% although there might be limitations and misclassification happened. Future recommendations have been highlighted to improve network’s performance in order to get better and more accurate result.
| Published in | American Journal of Networks and Communications (Volume 2, Issue 1) |
| DOI | 10.11648/j.ajnc.20130201.12 |
| Page(s) | 9-16 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2013. Published by Science Publishing Group |
Heart, ECG Signal, Features Extraction, Neural Network And Matlab Simulation
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| [5] | Rajesh Ghongade, Vishwakarma, Dr. Babasaheb, A brief Performance Evaluation of ECG Feature Extraction Techniques for Artificial Neural Network Based Classification. |
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APA Style
Tarmizi Amani Izzah, Syed Sahal Nazli Alhady, Umi Kalthum Ngah, Wan Pauzi Ibrahim. (2013). A Journal of Real Peak Recognition of Electrocardiogram (ECG) Signals Using Neural Network. American Journal of Networks and Communications, 2(1), 9-16. https://doi.org/10.11648/j.ajnc.20130201.12
ACS Style
Tarmizi Amani Izzah; Syed Sahal Nazli Alhady; Umi Kalthum Ngah; Wan Pauzi Ibrahim. A Journal of Real Peak Recognition of Electrocardiogram (ECG) Signals Using Neural Network. Am. J. Netw. Commun. 2013, 2(1), 9-16. doi: 10.11648/j.ajnc.20130201.12
AMA Style
Tarmizi Amani Izzah, Syed Sahal Nazli Alhady, Umi Kalthum Ngah, Wan Pauzi Ibrahim. A Journal of Real Peak Recognition of Electrocardiogram (ECG) Signals Using Neural Network. Am J Netw Commun. 2013;2(1):9-16. doi: 10.11648/j.ajnc.20130201.12
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Download@article{10.11648/j.ajnc.20130201.12,
author = {Tarmizi Amani Izzah and Syed Sahal Nazli Alhady and Umi Kalthum Ngah and Wan Pauzi Ibrahim},
title = {A Journal of Real Peak Recognition of Electrocardiogram (ECG) Signals Using Neural Network},
journal = {American Journal of Networks and Communications},
volume = {2},
number = {1},
pages = {9-16},
doi = {10.11648/j.ajnc.20130201.12},
url = {https://doi.org/10.11648/j.ajnc.20130201.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20130201.12},
abstract = {This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. All these waves are represented respective heart functions. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. These features will then be fed as an input to neural network system. The target output represented real peaks of the signals is also being defined using a binary number. Result obtained showing that neural network pattern recognition is able to classify and recognize the real peaks accordingly with overall accuracy of 81.6% although there might be limitations and misclassification happened. Future recommendations have been highlighted to improve network’s performance in order to get better and more accurate result.},
year = {2013}
}
TY - JOUR T1 - A Journal of Real Peak Recognition of Electrocardiogram (ECG) Signals Using Neural Network AU - Tarmizi Amani Izzah AU - Syed Sahal Nazli Alhady AU - Umi Kalthum Ngah AU - Wan Pauzi Ibrahim Y1 - 2013/02/20 PY - 2013 N1 - https://doi.org/10.11648/j.ajnc.20130201.12 DO - 10.11648/j.ajnc.20130201.12 T2 - American Journal of Networks and Communications JF - American Journal of Networks and Communications JO - American Journal of Networks and Communications SP - 9 EP - 16 PB - Science Publishing Group SN - 2326-8964 UR - https://doi.org/10.11648/j.ajnc.20130201.12 AB - This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. All these waves are represented respective heart functions. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. These features will then be fed as an input to neural network system. The target output represented real peaks of the signals is also being defined using a binary number. Result obtained showing that neural network pattern recognition is able to classify and recognize the real peaks accordingly with overall accuracy of 81.6% although there might be limitations and misclassification happened. Future recommendations have been highlighted to improve network’s performance in order to get better and more accurate result. VL - 2 IS - 1 ER -
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