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Ghongade, R. B.
- Deciding Optimal Number of Exemplars for Designing an ECG Pattern Classifier Using MLP
Abstract Views :462 |
PDF Views:75
Authors
Affiliations
1 Vishwakarma Instt. of Information Technology, Pune, IN
2 Dr. Babasaheb Ambedkar Technological Univ , Lonere
1 Vishwakarma Instt. of Information Technology, Pune, IN
2 Dr. Babasaheb Ambedkar Technological Univ , Lonere
Source
Indian Journal of Science and Technology, Vol 2, No 4 (2009), Pagination: 40-42Abstract
ECG pattern recognition using artificial neural networks is now an established paradigm. Diagnostic systems derive robustness, reliability and speed because of the automatic pattern classifiers. However, a common problem associated with these types of classifiers is to decide the optimal number of exemplars. This paper attempts to find an optimal number of exemplars required for training a multilayer perceptron with acceptable accuracy. Extensive experimentation suggests a figure of 200. Although this figure is specific for multilayer perceptron based classifier, experimentation on similar lines can be performed for other ANN topologies.Keywords
ECG, MLP, Pattern Classifier, Optimal Number of ExemplarsFull Text
References
- Fabian Vargas, Djones Lettin, Maria Cristina Felippetto de Castro, Marcello Macarthy (2002) Electrocardiogram pattern recognition by means of MLP network and PCA: a case study on equal amount of input signal types, IEEE. Proceedings of VII Brazilian Symposium on Neural Networks, 2002, 2, 200– 205.
- Gholam Hosseini H, Luo D and Reynolds KJ (2006) The comparison of different feed forward neural network architectures for ECG signal diagnosis. Medical Engg. & Physics, 28 (4), 372–378.
- Jiang W and Kong SG (2007) Block-based neural networks for personalized ECG signal classification. IEEE Trans. on Neural Networks.18,1750– 1761.
- Kemal Polat and Salih Güneş (2007) Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine. Appl. Maths. & Comput. 186 (1), 898-906.
- Krishna Prasad G, Shahambi JS (2003) Classification of ECG arrhythmias using multi-resolution analysis and neural networks, IEEE,1, 227-231.
- Lin He, Wensheng Hou, Xiaolin Zhen and Chenglin Peng (2006) Recognition of ECG patterns using artificial neural network. 6th Intl. Conf. on Intelligent Systems Design & Applns. 2, 477– 481.
- Rahime Ceylan and Yüksel Özbay (2007) Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Systems with Applns. 33, 286-295.
- Tadejko Pawel and Rakowski Waldemar (2007) Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification. 6th Intl. Conf. on Computer Info. Systems & Industrial Mangt. Applns. pp: 322–327.
- Optimization of a Multi-class MLP ECG Classifier Using FCM
Abstract Views :356 |
PDF Views:109
Authors
Affiliations
1 Vishwakarma Institute of Information Technology, Pune- 411048, IN
2 Dr. Babasaheb Ambedkar Technological University, Lonere- 402103, IN
1 Vishwakarma Institute of Information Technology, Pune- 411048, IN
2 Dr. Babasaheb Ambedkar Technological University, Lonere- 402103, IN
Source
Indian Journal of Science and Technology, Vol 3, No 10 (2010), Pagination: 1102-1105Abstract
ECG pattern classification using MLP is an effective and robust technique. Due to the inherent structure of MLP and training algorithm, MLPs tend to be slow and bulky in terms of the hidden layer neurons. This condition is aggravated further if the input data dimension is large as in the case of ECG. The paper addresses this problem by optimizing the MLP using an additional clustering of the feature extracted data. Considerable reduction in the size of MLP was recorded (max. 67.9%) with a reduction in training time (maximum of 59.81%). The experimentation used benchmark arrhythmia database from Physionet Massachusetts institute of technology-Beth Israel hospital (MIT-BIH). Four feature extraction methodologies were subjected to fuzzy c-means clustering for obtaining optimized MLPs. Ten statistical morphological features were also considered for designing the MLP classifiers.Keywords
ECG, MLP, DCT, DWT, FCM, Morphological FeaturesFull Text
References
- Bortolan G, Brohet C and Fusaro S (1996) Possibilities of using neural networks for ECG classification. J. Electrocardiol. 29, 10-16.
- Ceylan R and Yüksel Özbay (2007) Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Systems Appl. 33, 286-295.
- De Chazel P and Reilly RB (2000) A comparison of the ECG classification performance of different feature sets. Computers Cardiol. 27, 327–330.
- Engin M, Musa Fedakar, Erkan Zeki Engin and Mehmet Korürek (2007) Feature measurements of ECG beats based on statistical classifiers. Measurement. 40, 904-912.
- Ghongade R and Ghatol AA (2008) A robust and reliable ECG pattern classification using QRS morphological features and ANN” at IEEE TENCON. TENCON, 1-6.
- Ghongade RB and Ghatol AA (2009) Deciding optimal number of exemplars for designing an ECG pattern classifier using MLP. Ind. J. Sci. Technol. 2(4), 40-42.
- Liang-Yu Shyu, Ying-Hsuan Wu and Hu W (2004) Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG. IEEE Trans. Biomed. Engg. 51, 1269–1273.
- Ozbay Y, Ceylan R and Karlik B (2006) A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Computers Biol. Med. 36, 376– 388.
- Sternickel K (2002) Automatic pattern recognition in ECG time series. Computer Methods Prog. Biomed. 68, 109–115.
- Vargas F, Lettnin D, De Castro MCF and Macarthy M (2002) Electrocardiogram pattern recognition by means of MLP network and PCA: A case study on equal amount of input signal types. Proc.VII Brazilian Symp. on Neural Networks. 2, 200–205.