Key result
A hardware/software co-designed ECG and PPG preprocessor achieved high accuracy compared to software simulation, with Pearson correlation coefficients of 0.9993 and 0.9982 for ECG and PPG, respectively.
Why the study?
Muscle signals, motion artifacts, and power line interference affect ECG and PPG signals during data acquisition, necessitating digital preprocessing systems for hardware implementation.
Effect estimate: Pearson correlation 0.9993 (ECG), 0.9982 (PPG)
The designed hardware/software co-design preprocessor accurately denoises ECG and PPG signals, providing a foundation for developing wearable devices for cardiac monitoring.
Hardware co-design enables efficient wearable ECG/PPG monitoring; leaves open real-world clinical validation before adoption.
This paper aims to design a digital system to pre-process electrocardiogram (ECG) and photoplethysmogram (PPG) signal for the purpose of hardware implementation. Muscle signal, motion artifacts, power line interference affect the biomedical signal during data acquisition. The proposed system focuses at removing the noises by designing infinite impulse response filter to remove power line noise and finite impulse response filter to eliminate other high and low frequency noises. At first the preprocessor is designed in Matlab to validate the simulation performance. Then the hardware is designed in xilinx system generator targeting Zedboard Zynq xc7z020-1clg484. Finally, we verified the hardware software codesign by comparing both outputs. For quantity based analysis different filtering techniques have been applied to determine the most optimized system in terms of resource utilization and power consumption. Pearson correlation coefficient of 0.9993 and 0.9982 have been found for ECG and PPG, respectively using Hamming filter technique for High and low pass filter. Root squared error for both signal has been also in the range of 10⁻²• These data validate the accuracy of the designed system providing quality assurance. Frequency spectrum also has been analyzed to ensure denoising of undesired signals. The designed preprocessor can be utilized for further analysis of the signals and designing digital systems & wearable devices for the detection of heart rate, cardiac diseases etc.
No takes yet. Share an insight, caveat, or question.
Chowdhury et al. (2023) studied ECG and PPG signal processing. Hardware/Software Co-design of an ECG-PPG Preprocessor vs. Software simulation (Matlab) was evaluated on Pearson correlation coefficient and root squared error between hardware and software outputs (Pearson correlation 0.9993 (ECG), 0.9982 (PPG)). A hardware/software co-designed ECG and PPG preprocessor achieved high accuracy compared to software simulation, with Pearson correlation coefficients of 0.9993 and 0.9982 for ECG and PPG, respectively.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: