I. Introduction
An Electrocardiogram (ECG) is an electrical tracing of the heart rhythm, rate, and conduction of heart contraction and resting phase that detects abnormalities such as blockages [1] . A cardiologist or trained clinician should be able to confirm the automated report and give a confirmed interpretation by analyzing the waveforms on the ECG tracing. ECG interpretation and diagnostic analysis require some level of characterization of the voltage in the cardiac volume, or at least across the surface of the heart/body which requires that the electrodes be placed appropriately, as well as to measure voltage accurately [2] . Before the heartbeats, the myocardium is excited first, and a weak current is generated during the excitation process, which is conducted to all parts of the body through the tissues. For normal 12-lead measurements misplacing electrodes by a couple of centimeters can be clinically significant [3 , 4] . Short vector measurements, like the rhythm devices, or Apple watch measurements have neither the placement, the number of vectors, or the reference measurements to make good diagnostic decisions [5] . It is important to understand that many things can be found on an EKG. Some of these are very important and some are not, depending on the circumstances of the patient [6] . EKGs can also be incredibly difficult to interpret when there is a large amount of electrical interference caused by patient movement/tremors and other things; this is a problem both for humans and computers. The accuracy of interpretive EKGs has been respectable since the 1990s, but even those with the best analytic algorithms are never 100% accurate [7 , 8] . Heart attacks with typical, well-evolved changes too are read well. The most frequent errors are suspected myocardial infarction. But this is expected because machines read ECGs using algorithms loaded in them and none are perfect. The monitoring of Heart rate (HR) is the immediate action taken by the clinicians towards cardiac health. To compute the HR, QRS complex detection from the ECG waveform is crucial [9] . ECG waveform is contaminated with various types of external noises. The utilization of the ECG module continually while performing daily activities, leads towards a high probability of contamination from the external noise. It might corrupt the signals obtained from these sensors, resulting in the beat detection process being error-prone and difficult to analyze. The various noises present in the ECG signal are electromyography (EMG), Baseline wander (BLW), and power line interference (PLI) [10] . Filters have developed a huge interest in the past few years due to their wide applications in several areas such as signal processing, video processing, noise reduction, signal enhancement, etc. Digital filter and Analog filter are the two types of filters. Digital filters are preferred mostly as compared to analog filters [11 , 12] . As it has the following advantages over analog filters like programmability, stability, sensitivity, accuracy, flexibility, etc. The basic block diagram of the Digital filter is shown in Fig 1 .