Key result
FIR filters using the Kaiser Window demonstrated excellent outcomes for removing baseline wandering and electromyography noise from ECG signals compared to other windowing methods.
The Kaiser windowing method for FIR filters provides superior ECG signal enhancement by effectively removing low and high-frequency interference.
Electrocardiogram (ECG) signal is generally corrupted by various artifacts like baseline wander, power line interference (50/60 Hz) and electromyography noise and these must be removed before diagnosis. The task propounded in this article is removal of low frequency interference i.e. baseline wandering and high frequency noise i.e. electromyography in ECG signal and digital filters are implemented to delete it. The digital filters accomplished are FIR with various windowing methods as of Rectangular, Hann, Blackman, Hamming, and Kaiser. The results received are at order of 300, 450, 600. The signal taken of the MIT-BIH database which contains the normal and abnormal waveforms. The work has been in MATLAB where filters are implemented in FDA Tool. The result received for entire FIR filters with various windows are evaluated the waveforms, power spectrums density, signal to noise ratio (SNR) and means square error (MSE) of the noisy and filtered ECG signals. The filter which shows the excellent outcomes is the Kaiser Window.
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Kumar et al. (2014) studied ECG signal artifacts. FIR filters with Kaiser Window vs. Other windowing methods (Rectangular, Hann, Blackman, Hamming) was evaluated on Signal to noise ratio (SNR) and mean square error (MSE). FIR filters using the Kaiser Window demonstrated excellent outcomes for removing baseline wandering and electromyography noise from ECG signals compared to other windowing methods.
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