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Published on January 25, 2008

Author: Carmela

Source: authorstream.com

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Adaptive Filters Applied to Heart ECG Brandon Beck and James Cotton:  Adaptive Filters Applied to Heart ECG Brandon Beck and James Cotton Introduction Analysis of mouse electrocardiogram Detect heart beat Work out heart period Resample heart rate Investigate heart rate variability Yield insight into physiological systems Detect contributions from parasympathetic and sympathetic neural systems Conclusion No filter performs best for all signals Linear filters perform better with linear manipulations and conditions Nonlinear filters perform better with nonlinear manipulations and conditions Acknowledgements DeBiasi Lab, Baylor College of Medicine Richard Baraniuk, Rice University Frequency Transforms Frequency Transforms we employed Fast Fourier Transform (FFT) Short Time Fourier Transform (STFT) Smoothed Pseudo Wigner-Ville (SPWV) Empirical Mode Decomposition (EMD) Beat Variability Controlled by the parasympathetic and sympathetic neural inputs Parasympathetic slows down the heart rate, appears at .4-1.6Hz on the spectrum Sympathetic speeds up the heart rate, appears at 1.6-3Hz on the spectrum Parsing Pros/Cons Pros Accurate when optimized Can extract beats from noise Cons Sensitive to parameters Complicated to implement Algorithm modifications are tricky Parsing Method Extracting pulse location from heart ECG using nonlinear analysis Determine initial heart beats using slope differential and amplitude thresholds Calculate heart rate and use it to predict the location of the next heart beat Select a heart beat that is closest to the prediction and is highest in amplitude If noise hinders the accurate selection of a heart beat, suspend output until appropriate Filtering Pros/Cons Pros Simple to implement Quick in Matlab By lowering threshold, can capture all beats Cons Less tolerant to noise Filtering Method Extracting pulse location from heart ECG using linear analysis Band pass filter to remove noise Select good heart pulse Use for match filter Generate threshold curve Measure interval between rising edges Beat Detection Must have high accuracy to be usable for heart rate variability study Must deal with high levels of noise and still be able to extrapolate where the beat might be

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