Abstract
Biomedical signal monitoring and recording are an integral part of medical diagnosis and treatment control mechanisms. For this, enhanced signals with appropriate peak preservation are required. The OWA (OrderedWeighted Aggregation) Filter used in this paper helps in non-linear signal filtering and preservation of peaks for accurate medical diagnosis. Weights are an important aspect of the OWA filter, the Gaussian method and the KDE (Kernel Density Estimation) function are used to obtain a precise output which helps in filtering the signal. This filter is further compared with another non-linear filter that is the median filter to understand the compatibility and the preciseness of the filter in a much deeper sense. OWA | filter | peak | kernel density estimation | probability density | EPD (Estimated Probability Density)
Highlights
When it comes to signal filtering, noise reduction is a crucial aspect
Since there is no practical demonstration of use of Kernel Density Estimation (KDE) for biomedical signal processing, this paper demonstrates how KDE can be Used practically
From the signal to noise ratios of different electroencephalogram signal (EEG) signals we can figure out that the OWA filter is capable of filtering signal while maintaining peaks without losing any data
Summary
When it comes to signal filtering, noise reduction is a crucial aspect. In this paper we consider the EEG signals for signal filtering and enhancement. Noise filtering, smoothing, and other signal improvement methods are used to improve the analytical signals’ signal-to-noise ratio. Any peak narrowing or deconvolution techniques often involve simultaneous filtering of the noise. Filtering can aid in the visual identification of the signal’s significant characteristics. For such signal filtering and enhancement, we use a novel nonlinear filter called the OWA filter. OWA filter is better known for its precise measurements and preservation of peak signals, through this paper we venture the field of signal enhancement using the OWA filter.[3] [4] [5]
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