FPGA based ECG Denoising: Current Status and Future Technologies

Abstract

The electrical action of the heart is recorded by an electrocardiogram signal (ECG). It records the electrical signal in order to check for various heart ailments and disorders. The precise computer analysis of ECG signals is difficult, as being a low-frequency signal, it is exceedingly vulnerable to high-frequency noise and numerous other losses. ECG filtering is therefore a major pre-processing step that attenuates the signal noise and converts it into a signal better suited for analysis. Researchers over a while have given various techniques to correctly detect and filter out the noises. In this paper, a number of state-of-the-art techniques for ECG signal pre-processing using FPGA on MT-BIH Arrhythmia and online datasets are used. There are a number of digital filters categories likewise FIR, IIR, and adaptive filtering techniques are present. The performance of these techniques are analysed on three parameters: Power Consumption, Signal-to-Noise Ratio, and Accuracy. It has been analysed that, the median filter attains the best SNR value by employing Neural Network-based ECG on FPGA board. From the analysis of the papers, we find that the median filter provides the best SNR value, Neural Network-based ECG Anomaly Detection on FPGA technique provides the best accuracy and the combination of FIR LPF & HPF using Kaiser and Bartlett windowing technique provides the lowest power consumption.