Gilbert, Markus Aminius and Shabrina, Nabila Husna and Wijaya, Andre and Wijaya, Jeremy Pratama (2020) Perbandingan Pola Sinyal Penyakit Myocardial Infarction dengan Jantung Normal Menggunakan Metode Wavelet Symlet. Ultima Computing : Jurnal Sistem Komputer, 12 (1). pp. 49-56. ISSN 2355-3286
Full text not available from this repository.Abstract
On the signal of healthy human cardiography and people with myocardial infarction contain a lot of noise. For this reason, it is necessary to process the appropriate signal so that the information contained by the signal can be detected easily. Stages of research include data search, pre-processing, signal processing with the denoise Wavelet Symlet method, and qualitative comparison of cardiographic signal patterns resulting from normal human signal processing with myocardial infarction. To eliminate noise on ECG signals, the denoise method using Wavelet Symlet is proven to be better than the Finite Impulse Response (FIR) Hamming Windows filter. MATLAB is one of the software options that can be used in processing cardiographic signals with denoise Wavelet Symlet method, proven to have a fairly high reliability based on qualitative analysis. This trial also proved that the pattern of human cardiographic signals with myocardial infarction disorders was quite random and and differed from one patient to another. However, slower heart rate patterns, greater amplitude, and abnormalities in the P, T signal and stretching Q-S distances can be a reference for diagnosing someone with myocardial infarction.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 004 Computer Science, Data Processing, Hardware 500 Science and Mathematic > 500 Science > 507 Education, Research, Related Topics |
| Divisions: | Faculty of Engineering & Informatics > Computer Engineering |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 02 Dec 2021 17:51 |
| Last Modified: | 29 Jun 2022 03:52 |
| URI: | https://kc.umn.ac.id/id/eprint/19343 |
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