Support vector machine and artificial neural network implementation in embedded systems for real time arrhythmias detection

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 Semiconductor and on a FPGA Spartan® 6 from Xilinx™, looking for real-time detection of ventricular tachycardia (VT) and ventricular fibrillation (VF). They were compared in terms of accuracy 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, labeled 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 time less than 0.6 ms in microcontroller and 30 μs in FPGA for ANN and less than 30 ms in a microcontroller for SVM. The test was done with a 32 MHz clock.