Key result
An algorithm using simple and moving averaging filters followed by polynomial curve fitting successfully denoised high-frequency noise from ECG signals with low computational cost.
A simple averaging and moving averaging filter combined with polynomial curve fitting provides a computationally efficient method for denoising high-frequency noise from ECG signals.
The Electro-CardioGram (ECG) is a graphical representation of electro-mechanical activities of the heart. It reflects the state of heart, and is very much useful in disease diagnosis. Since, the ECG signal contains high frequency noise, is well known as power line interference. Hence, it must be removed for further processing. This paper presents an algorithm, developed for denoising high frequency noise from ECG signal which is based on a simple averaging and a moving averaging filter. The filtering process is followed by an algorithm for smoothing the ECG signal using polynomial curve fitting. It’s denoising performance is implemented, smoothened, and compared in the C++ environment. The proposed algorithm does require redundant preprocessing steps, thus allowing a simple architecture for its implementation as well as low computational cost.
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Vishakha Pandey (2015) studied ECG signal noise. Denoising algorithm (averaging filters and polynomial curve fitting) was evaluated on Denoising performance. An algorithm using simple and moving averaging filters followed by polynomial curve fitting successfully denoised high-frequency noise from ECG signals with low computational cost.
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