Research Article ECG Paper Digitization and R Peaks Detection Using FFT Ibraheam Fathail 1 and Vaishali D. Bhagile 2 1 Faculty of Computer Sciences & IT, Hajjah University, Hajjah, Yemen 2 Department of Computer Science and IT, Animation Deogiri College, Aurangabad Affiliated to Dr. B.A.M.U, Aurangabad, India Correspondence should be addressed to Ibraheam Fathail; abumarina2223@gmail.com Received 8 August 2022; Revised 2 September 2022; Accepted 5 September 2022; Published 10 October 2022 Academic Editor: Manikandan Ramachandran Copyright © 2022 Ibraheam Fathail and Vaishali D. Bhagile. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An electrocardiogram (ECG) uses electrodes to monitor the heart rhythm and identify minute electrical changes that occur with each beat. It is employed to investigate particular varieties of aberrant heart activity, such as arrhythmias and conduction problems. One of the most essential tools for detecting heart problems is the electrocardiogram (ECG). e majority of ECG records are still on paper. Manual ECG paper record analysis can be difficult and time-consuming. It is possible to digitally digitize these paper ECG recordings for automated analysis and diagnosis. In this paper, we proposed a system to digitize the ECG paper, automatically detecting R peaks, calculating the average heart rate, and sending SMS to the doctor via cloud in the event of detection of abnormality. e method of the system is uploading an ECG image, then dimensionality reduction, feature extraction in the form of digital signals, and saving it in a CSV file format using the MATLAB programming language. After that, the system retrieves the signals for further processing of the raw signals. We used the fast Fourier transform (FFT) algorithm to calculate R peaks and calculate the heart rate. If the heart rate is abnormal, the system sends SMS messages to doctors via a technology platform (Twilio) using the Python programming language. 1. Introduction Heart disease is the major reason of the high number of deaths in the world [1]. According to the WHO, heart disease remains the major reason of death globally, ac- counting for 16% of all deaths from all causes. It kills more people today than ever before, with the number of deaths from heart disease rising by more than two million since 2000, reaching nearly nine million in 2019 [2]. One of the standards for detecting cardiac problems is electrocardio- gram (ECG) analysis. ECG is a capture of electrical action in the heart of the muscle of the heart over the course of one cardiac cycle [3, 4]. e rhythmic depolarization and re- polarization of the myocardium associated with the con- tractions of the atria and ventricles during each cardiac cycle are represented by a recurring sequence of P, QRS, T, and a conditional U wave [5] (Figure 1). ECG is a semiperiodic, rhythmically, and concurrent signal with a cardiac task that is obtained using a passive sensory instrument that functions as a generator of bioelectric signals, simulating the heart’s function [6]. e ECG signals are intrinsically low and noisy signals made up of numerous changeable components due to a variety of environmental conditions such as variations in body tem- perature, body movement, and line frequency (50/60 Hz), among others. Because the ECG signal cannot be directly conditioned, amplified, or replicated, digital filtering tech- niques with configurable windows are utilized [7]. Typically, clinicians examine the ECG signal visually, looking at its form, rhythm, and voltage. Digital signal processing, in combination with classical or advanced machine learning, is currently playing a major part in medical diagnosis, particularly in ECG diagnosis. However, most commercial ECG devices do not allow raw data, so it is often limited. One way to approach this challenge is to use a classification system based on ECG image processing. e ECG monitoring system’s main source of power usage is wireless transmission of ECG data. As a result, a solution that decreases the size while maintaining signal quality integrity is required. An efficient compression approach can Hindawi Applied Computational Intelligence and So Computing Volume 2022, Article ID 1238864, 11 pages https://doi.org/10.1155/2022/1238864