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Authors: Andrés Orozco-Duque ; Santiago Rúa ; Santiago Zuluaga ; Alfredo Redondo ; Jose V. Restrepo and John Bustamante

Affiliation: Universidad Pontificia Bolivariana, Colombia

ISBN: 978-989-8565-36-5

Keyword(s): Arrhythmias, Artificial Neural Network, ECG signal, FPGA, Microcontroller, Support Vector Machine.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Real-Time Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods ; Wavelet Transform

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 several formats:
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

@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},
}

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

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