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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. Filtering Pros/Cons Pros Simple to implement Quick in Matlab By lowering threshold, can capture all beats Cons Less tolerant to noise. Beat Variability Controlled by the parasympathetic and sympathetic neural inputs

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Adaptive Filters Applied to Heart ECG Brandon Beck and James Cotton

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  1. Adaptive Filters Applied to Heart ECGBrandon Beck and James Cotton • Filtering Pros/Cons • Pros • Simple to implement • Quick in Matlab • By lowering threshold, can capture all beats • Cons • Less tolerant to noise • 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 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 • 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 • 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 • Frequency Transforms • Frequency Transforms we employed • Fast Fourier Transform (FFT) • Short Time Fourier Transform (STFT) • Smoothed Pseudo Wigner-Ville (SPWV) • Empirical Mode Decomposition (EMD) • 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 • Parsing Pros/Cons • Pros • Accurate when optimized • Can extract beats from noise • Cons • Sensitive to parameters • Complicated to implement • Algorithm modifications are tricky • 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

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