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We propose a multiple initialization based spectral peak tracking (MISPT) technique for heart rate monitoring from
photoplethysmography (PPG) signal.MISPT is applied on the PPG signal after removing the motion artifact using an adaptive noise cancellation filter. MISPT yields several estimates of the heart rate trajectory from the spectrogram of the denoised PPG signal which are finally combined using a novel measure called
trajectory strength. Multiple initializations help in correcting


We considered the problem of accurately estimating the heart rate (HR) using photoplethysmography (PPG) signals that are contaminated by motion artifacts (MA). A novel HR estimation approach based on GRidless spectral Estimation and SVM-based peak Selection, denoted by GRESS, was proposed. It first obtained the sparse spectrum of PPG using a continuous dictionary, then a simple spectral subtraction step was adopted to remove MA, finally an SVM-based method was developed to select the spectral peak corresponding to HR.


Critical to accurate reconstruction of sparse signals from low-dimensional low-photon count observations is the solution of nonlinear optimization problems that promote sparse solutions. In this work, we explore recovering high-resolution sparse signals from low-resolution measurements corrupted by Poisson noise using a gradient-based optimization approach with non-convex regularization. In particular, we analyze zero-finding methods for solving the p-norm regularized minimization subproblems arising from a sequential quadratic approach.


We introduce a novel, transient model for the electroencephalogram (EEG) as the noisy addition of linear filters responding to trains of delta functions. We set the synthesis part as a parameter-tuning problem and obtain synthetic EEG-like data that visually resembles brain activity in the time and frequency domains. For the analysis counterpart, we use sparse approximation to decompose the signal in relevant events via Matching Pursuit.