The effect of denoising on classification of ECG signals

Electrocardiogram (ECG) signals are affected by different types of noise and artifacts which can hide significant information related to heart functioning. Therefore, denoising of signals is essential for accurate diagnosis of different heart diseases. This study examines and compares three different denoising techniques, namely Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Multiscale Principal Component Analysis (MSPCA), and their influence on ECG signal classification. In order to extract significant features from ECG signals, wavelet packet decomposition (WPD) technique is employed. To classify ECG signals, ensemble machine learning classifier called Rotation Forest is proposed for classification task. In this study, three different systems, namely PCA-WPD-Rotation Forest, ICA-WPD-Rotation Forest and MSPCA-WPD-Rotation Forest were analyzed and evaluated in terms of three different statistical metrics, namely F-measure, AUC value and overall accuracy. To evaluate performances of proposed systems, MIT-BIH database is used. Obtained experimental results showed that MSPCA-WPD-Rotation Forest system resulted in the highest performances when compared to PCA-WPD-Rotation Forest and ICA-WPD-Rotation Forest. Therefore, in this study following system for classification of ECG signals is proposed: MSPCA as the first component for denoising, WPD as the second component which extracts important features from ECG signals and Rotation Forest as the third component which performs classification of ECG signals. The comparison to other systems for classification of EEG signals shown that proposed system outperforms other systems on the MIT-BIH arrhythmia database under investigated conditions.