Arrhythmia warning algorithm
Arrhythmias are cardiac conduction dysfunctions that cause abnormal heartbeats, which can be detected through electrocardiographic (ECG) signals. However, visually analyzing ECG signals can be challenging and time-consuming due to the low amplitudes involved. To address this issue, an automate...
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Nanyang Technological University
2023
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sg-ntu-dr.10356-1667152023-05-12T15:37:01Z Arrhythmia warning algorithm Tan, Calvin Jun Hao Vidya Sudarshan School of Computer Science and Engineering vidya.sudarshan@ntu.edu.sg Engineering::Computer science and engineering Arrhythmias are cardiac conduction dysfunctions that cause abnormal heartbeats, which can be detected through electrocardiographic (ECG) signals. However, visually analyzing ECG signals can be challenging and time-consuming due to the low amplitudes involved. To address this issue, an automated system can be utilized in the clinical context to improve the speed and accuracy of arrhythmia diagnosis. This report presents a proposed ensemble model comprising convolutional neural network (CNN) and Support Vector Machine (SVM) to automate the identification of arrhythmias in ECG data. The model's hyperparameters will be optimized to achieve the best performance, with solutions presented for any problems encountered in the methods chapter. The report also includes the demonstration and assessment of the model's performance on several datasets. Bachelor of Engineering (Computer Science) 2023-05-09T07:38:43Z 2023-05-09T07:38:43Z 2023 Final Year Project (FYP) Tan, C. J. H. (2023). Arrhythmia warning algorithm. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166715 https://hdl.handle.net/10356/166715 en SCSE22-0488 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Tan, Calvin Jun Hao Arrhythmia warning algorithm |
description |
Arrhythmias are cardiac conduction dysfunctions that cause abnormal heartbeats, which can
be detected through electrocardiographic (ECG) signals. However, visually analyzing ECG
signals can be challenging and time-consuming due to the low amplitudes involved. To
address this issue, an automated system can be utilized in the clinical context to improve the
speed and accuracy of arrhythmia diagnosis. This report presents a proposed ensemble model
comprising convolutional neural network (CNN) and Support Vector Machine (SVM) to
automate the identification of arrhythmias in ECG data. The model's hyperparameters will be
optimized to achieve the best performance, with solutions presented for any problems
encountered in the methods chapter. The report also includes the demonstration and
assessment of the model's performance on several datasets. |
author2 |
Vidya Sudarshan |
author_facet |
Vidya Sudarshan Tan, Calvin Jun Hao |
format |
Final Year Project |
author |
Tan, Calvin Jun Hao |
author_sort |
Tan, Calvin Jun Hao |
title |
Arrhythmia warning algorithm |
title_short |
Arrhythmia warning algorithm |
title_full |
Arrhythmia warning algorithm |
title_fullStr |
Arrhythmia warning algorithm |
title_full_unstemmed |
Arrhythmia warning algorithm |
title_sort |
arrhythmia warning algorithm |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/166715 |
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1770567566740160512 |