Rifqi Fauzi, Muhammad (2025) Sistem Akuisisi dan Klasifikasi Aritmia Menggunakan Machine Learning. S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.
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Abstract
Arrhythmia is a disruption in heart rhythm that can indicate one of the deadliest diseases in the world which is heart disease. Diagnosing arrhythmia is often done by recording and analyzing signals using an ECG (Electrocardiogram). However, ECG devices are expensive, and interpreting the waveforms requires specialized expertise. Therefore, a compact, affordable, and portable device was developed to acquire signals and classify arrhythmias. The Hilbert Transform is employed for detecting R-peaks, which serve as annotation points for RR interval segmentation during the preprocessing stage. These segmented intervals are then used as input for arrhythmia classification using machine learning. The classification model achieved an accuracy of 98.11%, with an average test accuracy of 98.2% on respondent data.
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | Aritmia merupakan gangguan pada irama jantung yang dapat mengidikasikan pada salah satu penyakit paling mematikan di dunia yaitu penyakit jantung. Untuk mengetahui adanya aritmia, salah satu metode yang biasa digunakan ialah dengan merekam dan menganalisa sinyal dengan ECG (Electrocardiogram) namun untuk membeli alat tersebut sangat mahal dan dibutuhkan ahli untuk yang mampu untuk membaca hasil gelombang pada sinyal jantung. Maka dari itu, dibuatlah alat untuk akuisisi sinyal dan melakukan klasifikasi aritmia yang murah, ringkas dan portable. Transformasi Hilbert digunakan untuk pencarian R peak yang dijadikan titik anotasi untuk proses segmentasi interval RR pada tahap preprocessing sehingga dapat digunakan sebagai input untuk klasifikasi aritmia menggunakan machine learning. Hasil akurasi untuk model klasifikasi aritmia yang digunakan didapatkan sebesar 98,11% dengan akurasi pengujian rata-rata pada data responden sebesar 98,2%. | |||||||||
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RZ Other systems of medicine |
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| Divisions: | 03-Fakultas Teknik > 20201-Jurusan Teknik Elektro | |||||||||
| Depositing User: | Muhammad Rifqi Fauzi | |||||||||
| Date Deposited: | 24 Jan 2025 08:31 | |||||||||
| Last Modified: | 24 Jan 2025 08:31 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/45732 |
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