Support Vector Machine and Artificial Neural Network Implementation in Embedded Systems for Real Time Arrhythmias Detection

Andrés Orozco-Duque, Santiago Rúa, Santiago Zuluaga, Alfredo Redondo, Jose V. Restrepo, John Bustamante

2013

Abstract

This article presents the development and implementation of an artificial neural network (ANN) and a support vector machine (SVM) on a 32-bit ARM Cortex M4 microcontroller core from Freescale Semiconductors and on a FPGA Spartan 6 from Xilinx. The ANN and SVM were implemented for real time detection of ventricular tachycardia (VT) and ventricular fibrillation (VF), and they were compared in terms of accu-racy and computational cost. A Fast Wavelet Transform (FWT) was used, and the energy in each sub-band frequency was calculated in the feature extraction stage. For the training and validation algorithms, signals from MIT-BIH database with normal sinus rhythm, VF and VT in a time window of 2 seconds were used.Test results achieve an accuracy of 99.46% for both ANN and SVM with execution times less than 0.6 ms in microcontroller and 30 µ s in FPGA for ANN and less than 30 ms in a microcontroller for SVM. The test was done with a 32 Mhz clock.

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Paper Citation


in Harvard Style

Orozco-Duque A., Rúa S., Zuluaga S., Redondo A., V. Restrepo J. and Bustamante J. (2013). Support Vector Machine and Artificial Neural Network Implementation in Embedded Systems for Real Time Arrhythmias Detection . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 310-313. DOI: 10.5220/0004232003100313


in Bibtex Style

@conference{biosignals13,
author={Andrés Orozco-Duque and Santiago Rúa and Santiago Zuluaga and Alfredo Redondo and Jose V. Restrepo and John Bustamante},
title={Support Vector Machine and Artificial Neural Network Implementation in Embedded Systems for Real Time Arrhythmias Detection},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={310-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004232003100313},
isbn={978-989-8565-36-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Support Vector Machine and Artificial Neural Network Implementation in Embedded Systems for Real Time Arrhythmias Detection
SN - 978-989-8565-36-5
AU - Orozco-Duque A.
AU - Rúa S.
AU - Zuluaga S.
AU - Redondo A.
AU - V. Restrepo J.
AU - Bustamante J.
PY - 2013
SP - 310
EP - 313
DO - 10.5220/0004232003100313