A Survey on Recent Research Trends Towards ECG Denoising for Sustainable Healthcare

Abstract: Cardiovascular diseases are a major cause of death and illness worldwide. This is why accurate Electrocardiogram (ECG) monitoring is so important for finding and treating these conditions early. Unfortunately, ECG signals can be unclear because of things like baseline drift, power line noise, movement, electrode issues, and electrical signals from muscles. This makes it harder to get a reliable diagnosis. To fix this, many methods have been developed to clean up ECG signals. These include digital filters, wavelet transforms, adaptive filters, deep learning, and empirical mode decomposition. When it comes to hardware, Field Programmable Gate Arrays (FPGAs) are a good choice for cleaning ECG signals in real time. They can process data in parallel, have low delay, can be reprogrammed, and use energy efficiently. This paper looks at recent methods for cleaning ECG signals using FPGAs. We compare their algorithms, how they are optimized for hardware, how complex they are to implement, what resources they use, and how well they remove noise. We also discuss how FPGAs are being used with Artificial Intelligence (AI), Edge Computing, the Internet of Things (IoT), and wearable health devices. Finally, we talk about the current research challenges and what needs to be done in the future to create ECG denoising systems that are scalable, use little power, and perform well.

Keywords: Electrocardiogram (ECG), FPGA, ECG Denoising, Wavelet Transform, Biomedical Signal Processing, Real- Time Signal Processing, Sustainable Healthcare, Wearable Healthcare.