Authors:
Andrés Orozco-Duque
;
Santiago Rúa
;
Santiago Zuluaga
;
Alfredo Redondo
;
Jose V. Restrepo
and
John Bustamante
Affiliation:
Universidad Pontificia Bolivariana, Colombia
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.