Partan, Radiyati Umi (2018) Reviewer_2_Radiyati_Umi_Partan_Cardiac Arrhythmias Classification Using Deep Neural Networksand Principle Component Analysis Algorithm. Int. J. Advance Soft Compu. A.
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Abstract
A primary diagnostic tool for cardiovascular diseases is electrocardiogram (ECG). The ECG evaluation for predicting cardiac arrhythmiasfrom databases, resulting in a comparable or even higher accuracy than experienced examiners. In this paper,automatic classification for cardiac arrhythmiais developed forproducing higher accuracy by using combination between DNNs andPCAtechnique. The algorithm developedbased onthe following stages such as,data preparation,datareduction, feature extractionand classification of rhythms. DNNs structure utilize Soft-max regression on the top of output layer and Cross-entropy as a cost function. To validate the method on the well-known MIT-BIH arrhythmia database is used. In the experiment 18 classifierstructure with several activation functions are created, to analyze the classifier performance. To benchmark, the performance of DNNs algorithm is compared to SVM algorithm in terms of accuracy. The result obtained show that the proposed method provides good accuracyagainstto MLPand SVM, about 97.7%, 95.56% and 79.51% respectivelywith less expert interaction.
| Item Type: | Other |
|---|---|
| Subjects: | R Medicine > RC Internal medicine > RC581-951 Specialties of internal medicine |
| Divisions: | 04-Faculty of Medicine > 11702-Internal Medicine (Sp |
| Depositing User: | dr radiyati umi partan |
| Date Deposited: | 21 Nov 2019 04:00 |
| Last Modified: | 21 Nov 2019 04:00 |
| URI: | http://repository.unsri.ac.id/id/eprint/17278 |
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