PUTRI, TRI FEBRIANI and Suprihatin, Bambang and Amran, Ali (2025) IMPLEMENTASI DISCRETE FOURIER TRANSFORM DAN ARSITEKTUR WAVENET – LONG SHORT-TERM MEMORY PADA KLASIFIKASI BEAT SINYAL ELEKTROKARDIOGRAM. Undergraduate thesis, Sriwijaya University.
![[thumbnail of RAMA_442001_08011382126095_Cover.jpg]](http://repository.unsri.ac.id/169738/10/RAMA_442001_08011382126095_Cover.jpg)  Preview |
Image
RAMA_442001_08011382126095_Cover.jpg - Cover Image Available under License Creative Commons Public Domain Dedication. Download (72kB) | Preview |
![[thumbnail of RAMA_442001_08011382126095.pdf]](http://repository.unsri.ac.id/style/images/fileicons/text.png) |
Text
RAMA_442001_08011382126095.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (19MB) |
|
Text
RAMA_442001_08011382126095_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (8MB) |
|
Text
RAMA_442001_08011382126095_0026017102_0013126603_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (5MB) |
|
Text
RAMA_442001_08011382126095_0026017102_0013126603_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) |
|
Text
RAMA_442001_08011382126095_0026017102_0013126603_03.pdf - Accepted Version Restricted to Registered users only Available under License Creative Commons Public Domain Dedication. Download (1MB) |
|
Text
RAMA_442001_08011382126095_0026017102_0013126603_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (9MB) |
|
Text
RAMA_442001_08011382126095_0026017102_0013126603_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (438kB) |
|
Text
RAMA_442001_08011382126095_0026017102_0013126603_06_ref.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) |
Abstract
In electrocardiogram (ECG) signal recordings, heartbeat rhythms can be normal and abnormal. One way to identify heart abnormalities in ECG signals is by classifying heartbeats into five classes, namely non ectopic beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). This study uses a combination of WaveNet architecture and Long Short-Term Memory (LSTM) for ECG signal classification, with the preprocessing stage using the Discrete Fourier Transform (DFT) method. The DFT method obtained an average SNR value of 25.21 dB which falls into the good category. This shows that the signal produced is good with low noise. Evaluation of model performance is done by measuring accuracy, sensitivity, specificity, precision, and F1-score. The accuracy value obtained is 99.60%, indicating that the model is able to classify the ECG signal into each class very well. Precision obtained 99.134% indicates that the model has a good level of accuracy in predicting arrhythmia disease for each class, so there are only a few classification errors. Sensitivity was 99.124%, indicating the model's good ability to recognize data belonging to the five classes. Specificity obtained 99.78% indicates that the model can recognize data that is not part of the five classes. F1-score obtained 99.134% indicates a balance between precision and sensitivity. Based on the evaluation results, the model shows excellent performance in recognizing class F compared to classes N, Q, S, and V with a sensitivity value of 100%. The values obtained in classes N, Q, S, and V are still below class F, but the results obtained are very good because the sensitivity values for classes S, V, and Q are more than 90%. Overall, the proposed model has shown excellent performance.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Signal denoising, Discrete Fourier Transform, ECG Signal Classification, WaveNet, LSTM |
| Subjects: | Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages |
| Divisions: | 08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1) |
| Depositing User: | Tri Febriani Putri |
| Date Deposited: | 21 Mar 2025 08:21 |
| Last Modified: | 21 Mar 2025 08:21 |
| URI: | http://repository.unsri.ac.id/id/eprint/169738 |
Actions (login required)
 |
View Item |