Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Wisnu Jatmiko; I Md. Agus Setiawan; Muhammad Ali Akbar; Muhammad Eka Suryana; Yulistiyan Wardhana; Muhammad Febrian Rachmadi

doi:10.7454/mst.v20i2.3060

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Wisnu Jatmiko, I Md. Agus Setiawan, Muhammad Ali Akbar, Muhammad Eka Suryana, Yulistiyan Wardhana, Muhammad Febrian Rachmadi

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.

Original language	English
Pages (from-to)	82-92
Journal	Makara Journal of Technology
Volume	20
Issue number	2
DOIs	https://doi.org/10.7454/mst.v20i2.3060
Publication status	Published - 1 Aug 2016

Keywords

arrhythmia, learning vector quantization, FN-GLVQ, FPGA

Access to Document

10.7454/mst.v20i2.3060Licence: CC BY-NC-SA

http://journal.ui.ac.id/technology/journal/article/view/3060Licence: CC BY-NC-SA

Cite this

@article{a7e8d0aa9c574235a82441d256c2dcaf,

title = "Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation",

abstract = "Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.",

keywords = "arrhythmia, learning vector quantization, FN-GLVQ, FPGA",

author = "Wisnu Jatmiko and Setiawan, {I Md. Agus} and Akbar, {Muhammad Ali} and Suryana, {Muhammad Eka} and Yulistiyan Wardhana and Rachmadi, {Muhammad Febrian}",

year = "2016",

month = aug,

day = "1",

doi = "10.7454/mst.v20i2.3060",

language = "English",

volume = "20",

pages = "82--92",

journal = "Makara Journal of Technology",

issn = "2356-4539",

number = "2",

}

TY - JOUR

T1 - Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

AU - Jatmiko, Wisnu

AU - Setiawan, I Md. Agus

AU - Akbar, Muhammad Ali

AU - Suryana, Muhammad Eka

AU - Wardhana, Yulistiyan

AU - Rachmadi, Muhammad Febrian

PY - 2016/8/1

Y1 - 2016/8/1

N2 - Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.

AB - Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.

KW - arrhythmia, learning vector quantization, FN-GLVQ, FPGA

U2 - 10.7454/mst.v20i2.3060

DO - 10.7454/mst.v20i2.3060

M3 - Article

SN - 2356-4539

VL - 20

SP - 82

EP - 92

JO - Makara Journal of Technology

JF - Makara Journal of Technology

IS - 2

ER -

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Abstract

Keywords

Access to Document

Fingerprint

Cite this