Denoising cyclostationary framework for enhanced Electrocardiogram analysis

We present a novel two module scheme for efficient analysis of noisy Electrocardiogram (ECG) signals. The first module consists of a segmentation algorithm which uses cyclostationary analysis for the detection of a single heart beat or cycle (P wave-QRS complex-T wave). The time domain cyclostationary (CS) algorithm exploits the statistical properties of the recorded periodic ECG signal and does not use any prior knowledge about signal morphology. Using the obtained cycle length the next module uses repeated applications of Principal Component Analysis (PCA) to reduce multiple additive noises from the multi trial and multi channel recorded ECG signals. PCA has been used for noise reduction in ECG but the method of repeated applications of PCA is novel. In this study, PCA was applied in 2 stages. In the first stage, PCA was applied to multi-channel ECG signals from one trial. The output ECG signals from the first stage were used in the second stage, where PCA was applied to multi-trial ECG signals from a single channel. The proposed scheme was tested with the 12-lead ECG signals from PTB Diagnostic database (National Metrology Institute of Germany) provided on physionet website which showed significant improvement in Signal to Noise ratio. We suggest that this simple scheme can be used for automatic analysis of noisy ECG signals where the extraction and denoising of single heart beat provide enhanced physiological features which enables better clinical interpretation of cardiovascular functionalities.