ECG denoising using Kaiser Bessel Window Filter

Thirrunavukkarasu, R.R.; Santhosh, K N S K; Shivaani, S; Devi, T. Meera; Srivardhini, R; Prabhu, Satish

doi:10.1109/icaccs51430.2021.9441958

Cited by 3 publications

(2 citation statements)

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“…Performance comparisons revealed Kaiser and Bartlett's window as the most effective in terms of resource utilization and power consumption. Thirrunavukkarasu et al (2021) designed an FIR filter using the Kaiser Bessel window for ECG waveform noise removal. The Kaiser filter implemented using the FPGA consists of three Verilog blocks connected by wires.…”

Section: Stage 1: Preprocessing (Denoising)mentioning

confidence: 99%

Real-Time Processing Stages of Electrocardiogram Signal: A Review

Waleed

2024

JMTE

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Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.

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Section: Stage 1: Preprocessing (Denoising)mentioning

confidence: 99%

Real-Time Processing Stages of Electrocardiogram Signal: A Review

Waleed

2024

JMTE

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“…Usually, the FIR filter is used in other works as well, for example, Sumathi et al, used the high pass least-square linear phase FIR filter to remove the artifacts from the original ECG signals in arrhythmia detection [9]. Other works that proposed the design of FIR filters for ECG signal filtering can be found in previous studies [10][11][12][13]. Most of the designs were implemented in the software environment such as MATLAB as it is a very comprehensive and convenient tool for digital signal processing.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of FIR Filter for ECG Signal Processing: Design, Optimization, and Performance Analysis on an FPGA

Chessda Uttraphan,

Mohd Izzul Ariffi Ardani,

Chua Wee Heng

et al. 2024

ARASET

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Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

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