Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
EURASIP Journal on Advances in Signal Processing
A source adaptive independent component analysis algorithm through solving the estimating equation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0s) and the shorter (0.556s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals.