Fast and Stable Signal Deconvolution via Compressible State-Space Models.

ABSTRACT:

Objective

Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred, and undersampled data, where the signals are in the form of discrete events distributed in time and space.

Methods

We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive l

SUBMITTER: Kazemipour A 

PROVIDER: S-EPMC5683949 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature