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Two effective ECG beat classification methods based on signal decomposition were compared in terms of effective feature selection and noise tolerance. The HOS-DWT-FFBNN method associated with the linear correlation based filter (LCBF) provides imposing capability to select the more representative features than the IC reordering method OWSL associated with the ICA-SVM method. Both methods are insensitive...
The performance of cognitive radio is sensitive to the accuracy of signal classification. The proposed method can increase the accuracy of existing methods on the certain degree at SNR=0 dB and below. In simulation, we classify five types of signals which are AM, BPSK, FSK, MSK and QPSK. The experiments show that above 99.9% received signals are correctly classified at SNR=-12 dB and above.
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
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