Neural network based ECG anomaly detection on FPGA and trade-off analysis

Abstract

This paper presents FPGA-based ECG arrhythmia detection using an Artificial Neural Network (ANN). The objective is to implement a neural network based machine learning algorithm on FPGA to detect anomalies in ECG signals, with a better performance and accuracy, compared to statistical methods. An implementation with Principal Component Analysis (PCA) for feature reduction and a multi-layer perceptron (MLP) for classification, proved superior to other algorithms. For implementation on FPGA, the effects of several parameters and simplification on performance, accuracy and power consumption were studied. Piecewise linear approximation for activation functions and fixed point implementation were effective methods to reduce the amount of needed resources. The resulting neural network with twelve inputs and six neurons in the hidden layer, achieved, in spite of the simplifications, the same overall accuracy as simulations with floating point number representation. An accuracy of 99.82% was achieved on average for the MIT-BIH database.