Electrocardiography Signal Analysis Using Neural Networks on FPGA

Electrocardiography (ECG) signal analysis is considered one of the core components of any integrated medical care systems. ECG diagnosis is one of the most valuable diagnostic tools. This book presents a proposed design for an integrated ECG diagnosing system. This system uses digital system processing techniques to analyze ECG signals. This methodology employs Highpass Least-Square Linear Phase Finite Impulse Response (FIR) filtering technique to remove the baseline wander noise embedded in the input ECG signal to the system or reduce the noise as much as possible. Discrete Wavelet Transform (DWT) was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network as a classifier to determine whether the input ECG signal represents normal or abnormal ECG signal. The whole system is implemented on Field Programming Gate Array (FPGA) board. Necessary simulations for the implemented system have been done, indicating that the implemented system has a good accuracy compared to other designs, achieving total accuracy of 97.8%, and achieving reduction in resources on FPGA implementation.