The Supervised Network Self-Organizing Map for Classification of Large Data Sets
Applied Intelligence
Intelligent data analysis
Computers & Mathematics with Applications
Adaptive wavelet network for multiple cardiac arrhythmias recognition
Expert Systems with Applications: An International Journal
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SEPADS'05 Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems
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Computer Methods and Programs in Biomedicine
IEEE Transactions on Neural Networks
Discrimination of ventricular arrhythmias using NEWFM
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Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias
Digital Signal Processing
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WH '10 Wireless Health 2010
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Journal of Medical Systems
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An ischemia detection method based on artificial neural networks
Artificial Intelligence in Medicine
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An improved procedure for detection of heart arrhythmias with novel pre-processing techniques
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ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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Hi-index | 35.69 |
The analysis of ECGs can benefit from the wide availability of computing technology. This paper presents some results achieved by carrying out the classification tasks of equipment integrating the most common features of the ECG analysis: arrhythmia, myocardial ischemia, chronic alterations. Several ANN architectures are implemented, tested, and compared with competing alternatives. The approach, structure, and learning algorithm of ANNs are designed according to the features of each particular classification task. The trade-off between the time consuming training of ANNs and their performance is also explored. Data pre- and post-processing efforts for system performance are critically tested. The crucial role of these efforts for the reduction of input space dimensions, for a more significant description of the input features, and for improving new or ambiguous event processing is also documented. Finally, algorithm assessment is done on data coming from available ECG databases