Key result
A methodology utilizing an 8th-order Butterworth IIR filter, Fractional Fourier Transform, and Particle Swarm Optimization effectively eliminates noise and improves feature extraction in ECG signals.
Why the study?
ECG signals often suffer from noise and artifacts that obscure vital diagnostic features.
The combination of higher-order filtering, Fractional Fourier Transform, and Particle Swarm Optimization provides an advanced methodology for improving ECG signal analysis and feature extraction.
This research paper presents a comprehensive methodology for Electrocardiogram (ECG) signal analysis utilizing advanced techniques including higher-order filtering, Fractional Fourier Transform (FrFT), and Particle Swarm Optimization (PSO). ECG signals, critical for diagnosing and monitoring cardiac health, often suffer from noise and artifacts that obscure vital diagnostic features. To address this, an 8th-order Butterworth Infinite Impulse Response (IIR) filter is used, which effectively eliminates noise while preserving essential signal characteristics such as P, QRS, and T waves. The FrFT is introduced to provide a flexible time-frequency representation, facilitating a more detailed analysis of non-stationary ECG signals. This transform allows for enhanced detection of transient cardiac events and improves the accuracy of feature extraction by adapting to the specific characteristics of the ECG signal. Furthermore, PSO is applied to optimize the feature selection process.
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Gupta et al. (2024) studied ECG signal analysis. Higher-order filtering, Fractional Fourier Transform (FrFT), and Particle Swarm Optimization (PSO) was evaluated. A methodology utilizing an 8th-order Butterworth IIR filter, Fractional Fourier Transform, and Particle Swarm Optimization effectively eliminates noise and improves feature extraction in ECG signals.
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