Neural Computing and Applications
Computers in Biology and Medicine
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents
Computer Methods and Programs in Biomedicine
Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias
Digital Signal Processing
Classification of the electrocardiogram signals using supervised classifiers and efficient features
Computer Methods and Programs in Biomedicine
ECG beat classification using particle swarm optimization and radial basis function neural network
Expert Systems with Applications: An International Journal
A patient-adaptive profiling scheme for ECG beat classification
IEEE Transactions on Information Technology in Biomedicine
A multi-stage automatic arrhythmia recognition and classification system
Computers in Biology and Medicine
Automated ECG diagnostic P-wave analysis using wavelets
Computer Methods and Programs in Biomedicine
A continuous mapping of sleep states through association of EEG with a mesoscale cortical model
Journal of Computational Neuroscience
Expert Systems with Applications: An International Journal
Feature selection for interpatient supervised heart beat classification
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
Computer Methods and Programs in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
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This study proposes a new method, equal frequency in amplitude and equal width in time (EFiA-EWiT) discretization, to discriminate between congestive heart failure (CHF) and normal sinus rhythm (NSR) patterns in ECG signals. The ECG unit pattern concept was introduced to represent the standard RR interval, and our method extracted certain features from the unit patterns to classify by a primitive classifier. The proposed method was tested on two classification experiments by using ECG records in Physiobank databases and the results were compared to those from several previous studies. In the first experiment, an off-line classification was performed with unit patterns selected from long ECG segments. The method was also used to detect CHF by real-time ECG waveform analysis. In addition to demonstrating the success of the proposed method, the results showed that some unit patterns in a long ECG segment from a heart patient were more suggestive of disease than the others. These results indicate that the proposed approach merits additional research.