Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications
Neural Networks
Atrial fibrillation classification with artificial neural networks
Pattern Recognition
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
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Atrial Fibrillation is the most common sustained arrhythmia encountered by clinicians. Because of the invisible waveform of atrial fibrillation in atrial activation for human, it is necessary to develop an automatic diagnosis system. 12-Lead ECG now is available in hospital and is appropriate for using Independent Component Analysis to estimate the AA period. In this research, we also adopt a second-order blind identification approach to transform the sources extracted by ICA to more precise signal and then we use frequency domain algorithm to do the classification. The strategy used in this research is according to prior knowledge and is different from the traditional classification approach which training samples are necessary for. In experiment, we gather a significant result of clinical data, the accuracy achieves 75.51%.