Key result
A Haar wavelet-based pre-processor design for ECG denoising consumed only 0.76% of LUTs, 5.03% of slice registers, 6.7% of DSPs, and 136 mW of on-chip power compared to other techniques.
Why the study?
ECG signal recording easily suffers from environmental interferences such as patient movement and electrode positioning, whereas noise-free ECG signals are needed for primary diagnosis.
A Haar wavelet-based FPGA implementation for ECG denoising demonstrates low resource utilization and power consumption, making it suitable for wearable biomedical devices.
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FPGA-based ECG denoising may aid real-time signal quality; extends hardware methods but leaves open clinical validation.
ECG signal recording easily suffers from interferences in the environment, such as patient movement, and position of electrodes. For primary diagnosis, doctors need noise-free ECG signals. This paper presents the hardware implementation of denoising of ECG signals using various types of linear windowing techniques and non-linear discrete wavelet transform (DWT) on the proposed architectures using FPGA. DWT has extensive usage in different image processing applications as image compression and information hiding. The simulation results obtained from various techniques and architectures are compared using MATLAB and XILINX VIVADO EDA tools. The performance evaluation of the proposed methodology is evaluated on the basis of resource utilization and on-chip power consumption on the different FPGA boards (Virtex, Kintex, and Zedboards) using VIVADO. From the simulation results, inference has been drawn that Haar wavelet consumes only 0.76% of LUTs, 5.03% of slice registers and 6.7% of DSPs in comparison with other wavelet and window techniques using Zedboard. The Haar wavelet based pre-processor design only consumes 136 mW of on-chip power. The proposed pre-processing module can be used in wearable and portable biomedical equipments.
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Kirti et al. (2022) studied ECG signal denoising. Haar wavelet based pre-processor design on FPGA vs. Other wavelet and window techniques was evaluated on Resource utilization and on-chip power consumption. A Haar wavelet-based pre-processor design for ECG denoising consumed only 0.76% of LUTs, 5.03% of slice registers, 6.7% of DSPs, and 136 mW of on-chip power compared to other techniques.
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