P, Q, R, S and T peaks recognition of ECG using MRBF with selected features
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Summary
Amplitude, duration, pre-gradient, post-gradient and peak degree were used as inputs to the individual RBF networks with selected features and overall accuracy of 86.53% achieved for identification of P, Q, R, S and T peaks.
- Type
- article
- Published
- 2005-02-13
- Cited by
- 6
- References
- 10
- OpenAlex
- https://openalex.org/W1530273493
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:56515607
Keywords
SIGNAL (programming language), Pattern recognition (psychology), Identification (biology), Artificial intelligence, Amplitude
References
- Some Properties of RBF Network with Applications to System Identification
- Neural networks for classification of ECG ST-T segments.
- Classification of arrhythmic events in ambulatory electrocardiogram, using artificial neural networks.
- One-lead ischemia detection using a new backpropagation algorithm and the European ST-T database
- Neural Networks For Pattern Recognition In Medical Diagnosis
- Artificial neural networks for automatic ECG analysis
- Networks for approximation and learning
- Preprocessing analysis and classification of electrocardiogram an artificial intelligence approach
- Hybrid training algorithm for RBF Network
- Diagnostic ECG classification based on neural networks.
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