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Klasifikasi Sinyal Ecgagal Jantung Menggunakan Wavelet Dan Jst Propagasi Balik Dengan Modifikasi Gradien Konjugat Polak-ribiere Heart Failure Ecg Signal Classification Using Wavelet and Ann Backpropagation with Polak-ribiere Conjugate Gradient Modification Method
| Content Provider | Semantic Scholar |
|---|---|
| Author | Komputasi, Ilmu |
| Copyright Year | 2018 |
| Abstract | Congestive Heart Failure (CHF) is one of the deadly diseases in the world due to abnormalities in heart muscles so the heart not able to pump the bloods according to the body needs. Heart signals can be detected using Electrocardiography (ECG). Basically, normal ECG signals has a similar shape. However, the ECG signals of CHF sufferers varying on each individual, so it can cause problems if the extraction process is done manually using local features. Therefore, wavelet feature extraction is used in this study because of its ability to perform frequency mapping over time. In addition, the classification process using ANN Backpropagation Standard method requires amount of time in training process. Thus, ANN Backpropagation with Modified Gradient Conjugate Polak-Ribiere with line search technique is proposed to speed up the searching process. At the end of the study, the feature was obtained by using WPD at 5 th level with 22 records of training data used. Gained an average value that is higher than the other trials, 72.5%. For the classification, known that 30 neurons in hidden layer and Charalambous' Search is the fastest search technique to be applied to this case with processing time 2.65 seconds, 14 epochs, and 87.5% accuracy. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://libraryeproceeding.telkomuniversity.ac.id/index.php/engineering/article/download/6116/6094 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
