Noise reduction and ECG feature extraction using interpolation and Hilbert transform

An effective and reliable noise reduction and Electrocardiogram (ECG) feature extraction algorithm is proposed in this paper. Contaminated ECG samples are de-noised using a Butterworth lowpass and IIR notch filter. First derivative using Lagrange Five Point Interpolation formula and Hilbert Transform of those ECG samples are computed. Sample having maximum amplitude is found out from the transformed data and those samples having amplitude within a lead wise specific threshold of that maximum are selected. The point where those selected samples undergo slope alteration in the original time domain ECG signal is marked as R peak. After successful identification of R peak points, base line modulation correction is implemented using an empirically determined formula. Q and S points are identified by finding minimum amplitude on the either side of the most recently detected R peak. QRS onset and offset points are also detected. After detecting all these characteristic points, Heart Rate, R, Q and S peak heights and QRS duration are measured. Errors in these extracted ECG features are also calculated. The algorithm offers a good level of Sensitivity (99.84%), Positive Predictivity (99.84%) and Detection Accuracy (99.84%) of R peak. Different types of ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB) is used for testing the performance of the proposed module.