Quantitative Investigation of Digital Filters in Electrocardiogram with Simulated Noises
Keywords:
Biomedical Signal Processing, Digital Filters, Electrocardiogram, Modeling and Analysis, Noise ReductionAbstract
Electrocardiogram (ECG) signal plays a vital role
in the primary diagnosis and monitoring of the health of heart. For the features extraction of the ECG signals such as R-peak, QRS complexes, T-waves etc., the significant noises have to be cancelled. The most significant noises corrupted the ECG signal are power line interference (50/60Hz), Electromyographic (EMG) noise due to motion artifacts, muscle contraction, baseline wanders due to respiration and perspiration, and instrumentation noise. Designing digital filters to suppress these noises sits in a quite important position for ECG signal processing and analysis. This paper presents the application of software digital filters in order to effectively eliminate these noises from the ECG signals. Several types of digital filters were designed and implemented along with their strengths and weaknesses. The quantitative properties of implemented digital filters were investigated with the ECG signals from MIT-BIH Arrhythmia Database as the test data. All the work was done with MATLAB®. The noises were simulated and added to the test data. The performance of digital filters was described by the comparison of power spectra of the filtered noisy signal and the original database ECG recordings and the mean square error.
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