Detection of Atrial Fibrillation using a Feedforward Sequential Model
Atrial Fibrillation (AFib) and its associated symptoms are significant problems that doctors and several studies have attempted to solve throughout the years. It is diagnosed by analyzing a patient’s electrocardiogram (ECG) data. However, continuous efforts have been made to develop an algorithm...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-12-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6684 |
| Summary: | Atrial
Fibrillation (AFib) and its associated symptoms are significant problems that
doctors and several studies have attempted to solve throughout the years. It is
diagnosed by analyzing a patient’s electrocardiogram (ECG) data. However,
continuous efforts have been made to develop an algorithm that detects AFib
with optimal efficiency and cost-effectiveness. In this study, a sequential
model was used based on feedforward neural network as this is arguably the
simplest algorithm developed and requires minimal computing power. The results showed that training the algorithm for 1000 epochs yielded the
best results. Further studies showed that using a combination of 10-fold
cross-validation and blindfold validation proved an ideal way to determine the
model's capabilities in distinguishing patients with AFib from those without.
In conclusion, the developed model successfully distinguished between AFib and
non-AFib patients with a 96.67% sensitivity, 94.61% specificity, and 95.64%
accuracy. |
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| ISSN: | 2086-9614 2087-2100 |