Arrhythmia classification from wavelet feature on FGPA

Jatmiko, Wisnu; Mursanto, Petrus; Febrian, Andreas; Fajar, Muhammad; Anggoro, Wahyu; Rambe, R.S.; Tawakal, Muhammad Iqbal; Jovan, Fauzi F.; MuthamilSelvan.S, None

doi:10.1109/mhs.2011.6102207

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…Our previous work includes designing Wavelet -FN-GLCQ to detect arrhythmia [31] and also to detect and estimate the level of Trichloroethylene inside a mouse liver [32]. Some fundamental modification of this implementation includes the additional design of sigmoid derivative core, expanding the data range from 12 bit to 32 bit, and the removal of operations that uses floating point computations.…”

Section: Methodsmentioning

confidence: 99%

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Jatmiko¹,

Setiawan²,

Akbar³

et al. 2016

MST

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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. AbstrakDeteksi Otomatis Detak Arrhythmia: Algoritma, Sistem dan Implementasi. Penyakit jantung adalah salah satu penyebab utama kematian di dunia. Diagnosa awal dari penyakit tersebut didasarkan pada ketidaknormalan pola detak jantung, atau disebut juga sebagai Arrhythmia. Sebuah metode baru bernama fuzzy neuro generalized learning vector quantization (FN-GLCQ) kami kembangkan untuk melakukan klasifikasi terhadap pola tersebut. Algoritma tersebut dimodifikasi dari algoritma GLVQ dengan mengimplementasikan konsep fuzzy logic sebagai fungsi diskriminan untuk meningkatkan performa klasifikasi. Algoritma tersebut dites dengan menggunakan MIT-BIH arrythmia database untuk dianalisis performanya. Berdasarkan uji coba yang telah dilakukan, FN-GLVQ dapat meningkatkan akurasi dari performa GLVQ. Sesuai dengan tujuan kami untuk membangun alat pendeteksian otomatis, FN-GLVQ juga diimplementasikan pada Field Gate Programmable Array sebagai prototipe dari perangkat yang akan dibangun.

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Section: Methodsmentioning

confidence: 99%

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Jatmiko¹,

Setiawan²,

Akbar³

et al. 2016

MST

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A Survey on Electrocardiogram Signal Detection and Classification on Hardware Platform

Devi

Singh

2022

2022 International Conference for Advancement in Technology (ICONAT)

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Efficient QRS Complex Detection Algorithm Implementation on Soc-Based Embedded System

Hashim

Hau

Baktheri³

2016

Jurnal Teknologi

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This paper studies two different Electrocardiography (ECG) preprocessing algorithms, namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection. Both algorithms are compared in terms of QRS detection accuracy and computation timing performance, with implementation on System-on-Chip (SoC) based embedded system that prototype on Altera DE2-115 Field Programmable Gate Array (FPGA) platform as embedded software. Both algorithms are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT-BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56.33 seconds computation while DB algorithm achieve 96.74% with only 22.14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result shows that the proposed optimized Moving Windows Integrator algorithm achieves 9.5 times speed up than original MWI while retaining its QRS detection accuracy.

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Arrhythmia classification from wavelet feature on FGPA

Cited by 6 publications

References 7 publications

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

A Survey on Electrocardiogram Signal Detection and Classification on Hardware Platform

Efficient QRS Complex Detection Algorithm Implementation on Soc-Based Embedded System

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