## 1 Introduction
### Digitization and Feature Extraction  
A modular feature extraction procedure will dispense with the access rights issues, which are likely to be encountered while using ECG machines with digitally recorded ECG data. It also enhances the utility value of ECG machines, which otherwise just generate ECG graphs by supplementing with a high sampling data extraction rate. Most of the ECG machines available in developing or underdeveloped countries do not allow sharing digital forms of ECG data. In the voltage versus time graph obtained from the electrocardiography, the automated analysis allows for the identification of ECG findings and the assessment of pathological anomalies without the need for human involvement. Digitization through image processing is one of the methods used to store ECG and TMT-ECG data [32]. The preprocessing of an image is primarily done based on pixel information. The pixel dimension of the image is determined by the size of the pixel array. The height and width of an image are determined by the number of rows (N) and columns (M) of the array. M X N is the size of the pixel array. The fundamentals of image processing for digitization are: image segmentation, color correction, point, line, and edge detection, aid in the region-dependent threshold system, the removal of paper noise with a filter, morphological image processing operations for image dilation, erosion, and pixel indexing to store the data.  
The ECG signal has to be interpreted in terms of specific quantitative features, i.e., P--the arterial systole contraction pulse, R--the peak of the ventricular contraction, S--the downward deflection immediately after the ventricular contraction, Q--the downward deflection immediately preceding the ventricular contraction, and T--the recovery of the ventricles. The method is defined in terms of locating the P, Q, R, S, and T waves in electrocardiograms, which then measure the R-peak [18; 33], PR, and R-R segments [21].  
The scanned image with optical character recognition (OCR) is another digitization technique. It uses the threshold method to eliminate gridlines [34] and the median filter to remove noise before converting the data to a 1D vector. The Tompkins algorithm [35] was created in 1985-1986 to find features, such as the QRS complex using the low pass and high pass filters, squaring signal, integration, and adaptive thresholding with an accuracy of 95%. The common approach adopted to obtain features of these ECG signals is the principal component analysis (PCA) [36], while the wavelet packet decomposition (WPD) method combined with statistical methods can yield better outcomes [37]. The digitization tool [32; 38] is used to digitize ECG graphs and measure features such as heart rate, QRS distance, R-R peak variance, and PQRS complex with 90% accuracy.  
Since interpreting variations of ECG or TMT-ECG manually is difficult, a computer-aided diagnostic system assisting in cardiac health monitoring is a better alternative. The nonlinear extraction approach (because of its nonlinear nature) is ideally suited for extracting information from the ECG signal [2]. The ECG processing system [21; 39] is capable of reading a wide range of pathological situations, including P, Q, R, S, T wave directions, a T wave greater than the R wave, and the absence of the Q wave in amplitude. Based on the wavelet transformation method [31], the QRS complex and QRS-T segments(30 ms before 240 ms after QRS position) are detected automatically. Thus, the selection of the best method delivers effective digitized ECG signal data by implementing the state-of-the-art technique.