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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Terpadu JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga)
Martin Clinton Tosima Manullang
Institut Teknologi Sumatera
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Implementation of AD8232 ECG Signal Classification Using Peak Detection Method For Determining RST Point Martin Clinton Tosima Manullang; Jonathan Simanjuntak; Ahmad Luky Ramdani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.711 KB) | DOI: 10.24014/ijaidm.v2i2.7593

Abstract

The medical world, especially those related to diseases and management of the heart uses ECG as a measurement tool. ECG has important points determined based on predetermined characteristics. The point is PQRST, where three of them are used as research objects in this paper. AD8232 is used as a research medium where the RST points must be determined in the AD8232 plot results by first determining the R points based on the highest peak. The results obtained were satisfactory wherein from 10 ECG graphic samples, 9 of them obtained RST point measurements which tended to be similar to conventional ECG measurements using millimeter paper as plotting media. Accuracy values reaching more than 90% indicate the reliability of the implementation results.
Co-Authors Istighfariza Aprini Jonathan Simanjuntak Muhamad Arwin Wijaya Nova Resfita Rahman Indra Kesuma Raidah Hanifah Ramdani, Ahmad Luky Rivaldo Fernandes