Deep learning based arrhythmia classification on low-cost and low-compute MCU
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| Język: | English |
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Brac University
2023
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| Dostęp online: | http://hdl.handle.net/10361/22018 |
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10361-22018 |
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10361-220182023-12-21T21:02:42Z Deep learning based arrhythmia classification on low-cost and low-compute MCU Zishan, Md Abu Obaida Shihab, H M Rahman, Gazi Mashrur Islam, Sabik Sadman Riya, Maliha Alam Mukta, Jannatun Noor Department of Computer Science and Engineering, Brac University ECG Arrhythmia Low-cost Low-compute Low-power Machine learning Micro-controller Cognitive learning theory (Deep learning) Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 56-57). 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. Md Abu Obaida Zishan H M Shihab Gazi Mashrur Rahman Sabik Sadman Islam Maliha Alam Riya B.Sc. in Computer Science 2023-12-21T05:58:55Z 2023-12-21T05:58:55Z 2023 2023-01 Thesis ID: 18201214 ID: 19101585 ID: 18241003 ID: 18301029 ID: 19101270 http://hdl.handle.net/10361/22018 en Brac 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. 57 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
ECG Arrhythmia Low-cost Low-compute Low-power Machine learning Micro-controller Cognitive learning theory (Deep learning) Machine learning |
| spellingShingle |
ECG Arrhythmia Low-cost Low-compute Low-power Machine learning Micro-controller Cognitive learning theory (Deep learning) Machine learning Zishan, Md Abu Obaida Shihab, H M Rahman, Gazi Mashrur Islam, Sabik Sadman Riya, Maliha Alam Deep learning based arrhythmia classification on low-cost and low-compute MCU |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Mukta, Jannatun Noor |
| author_facet |
Mukta, Jannatun Noor Zishan, Md Abu Obaida Shihab, H M Rahman, Gazi Mashrur Islam, Sabik Sadman Riya, Maliha Alam |
| format |
Thesis |
| author |
Zishan, Md Abu Obaida Shihab, H M Rahman, Gazi Mashrur Islam, Sabik Sadman Riya, Maliha Alam |
| author_sort |
Zishan, Md Abu Obaida |
| title |
Deep learning based arrhythmia classification on low-cost and low-compute MCU |
| title_short |
Deep learning based arrhythmia classification on low-cost and low-compute MCU |
| title_full |
Deep learning based arrhythmia classification on low-cost and low-compute MCU |
| title_fullStr |
Deep learning based arrhythmia classification on low-cost and low-compute MCU |
| title_full_unstemmed |
Deep learning based arrhythmia classification on low-cost and low-compute MCU |
| title_sort |
deep learning based arrhythmia classification on low-cost and low-compute mcu |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/22018 |
| work_keys_str_mv |
AT zishanmdabuobaida deeplearningbasedarrhythmiaclassificationonlowcostandlowcomputemcu AT shihabhm deeplearningbasedarrhythmiaclassificationonlowcostandlowcomputemcu AT rahmangazimashrur deeplearningbasedarrhythmiaclassificationonlowcostandlowcomputemcu AT islamsabiksadman deeplearningbasedarrhythmiaclassificationonlowcostandlowcomputemcu AT riyamalihaalam deeplearningbasedarrhythmiaclassificationonlowcostandlowcomputemcu |
| _version_ |
1814307951442132992 |