Supplementary Table S1: Time-series ECG data of arrhythmic patients and normal controls smoothened by Savitzky-Golay filter.
Full-length ECG time series data of 48 arrhythmic patients has been collected from popular MIT-BIH Arrhythmia Database [1-3]. The data sets include ECG time series data of 25 men aged between 32 to 89 years and of 22 women aged between 23 to 89 years. On the other hand, the normal data (healthy person) has been collected from the MIT-BIH Normal Sinus Rhythm Database  consisting of 18 long-term ECG signals of subjects having no significant arrhythmia. The subjects include 5 men and 13 women, aged between 26 to 45 and 20 to 50 respectively. The signal used here for the analysis is a modified limb lead II (MLII), obtained by placing the electrodes on the chest of the patients. We did not include the records of two patients with patient IDs 102 and 104 from MIT-BIH Arrhythmia Database as the required MLII data were not available due to surgical dressings on the above-mentioned patients. On the other hand, we also consider the data from ECG1 mode which are ECG signals (Normal Sinus Rhythm Database) relating to healthy persons and these data sets are regarded as complementary to MLII data of the arrhythmia database. Here, total no. of data points of each of the disease data series is 21600 with frequency 360.01 per sec whereas the same for the normal data series is 7680 with frequency 128 per sec. Therefore, each of the data series is recorded for 60 sec time duration. A filter is utilized for processing of signals in order to selectively isolate a particular frequency or range of frequencies from an assortment of multiple frequencies in a signal. The choice of appropriate filter for processing of the system generated signals requires maximum noise reduction with minimal signal distortion . One of the best filters for noise clearing of biomedical data, including ECG signals, seems to be Savitzky–Golay (SG) filter . The fundamental principle of SG filter is to consider (2n + 1) equidistant points taking n = 0 as a centre to represent a polynomial of degree p (where p < (2n + 1)). A set of points is to be fitted to some curve. For this purpose, SG filter computes the value of the least square polynomial (or its derivative) at a point, i = 0, over the decided frame range. This filter applies the method of linear least squares for data smoothing, which helps to maintain the original shape of the signal. The SG filter generally requires pre-determined values of order and frame depending on the frequency and length of the data. Usually, trial and error method or prior experience is required to decide the satisfactory values of parameters. Here, the values of “Frame” for diseased and normal data were assumed to be 37 and 13 respectively and the “Order” of the filter were 3 for each type of data sets. The ‘Supplementary Table SI’ contains the filtered data sets for both types of disease and normal subject.