Mukta, Jannatun NoorZishan, Md Abu ObaidaShihab, H MRahman, Gazi MashrurIslam, Sabik SadmanRiya, Maliha Alam2023-12-212023-12-2120232023-01ID: 18201214ID: 19101585ID: 18241003ID: 18301029ID: 19101270http://hdl.handle.net/10361/22018Cataloged from PDF version of thesis.Includes bibliographical references (pages 56-57).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.According to WHO, cardiovascular disease (CVD) is the leading cause of death glob ally. Unfortunately, these diseases are difficult to diagnose without proper equip ment which is not cheap. One of the reasons for such a high cost of treatment is the use of expensive technologies like ECG or electrocardiograph monitoring systems. These monitoring systems are usually implemented using expensive high-compute hardware and proprietary algorithms. Conventional ECG systems cost between $2000 and $10,000. But in theory, these systems can also be developed through low-compute hardware (such as microcontrollers or FPGA) and machine learning. This paper performs a comparative study on the implementation of low-cost, low power, and low-compute-based ECG systems and analyzes better approaches for future design. Additionally, it implements an ECG monitoring system based on that approach.57 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.ECGArrhythmiaLow-costLow-computeLow-powerMachine learningMicro-controllerCognitive learning theory (Deep learning)Machine learningThesis