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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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/166715 |
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| 總結: | 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. |
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