Input Sequence and Parameter Estimation in Impulsive Biomedical Models

TL;DR

This paper presents an improved method for jointly estimating input sequences and system parameters in impulsive biomedical models, enhancing accuracy and usability over previous approaches.

Contribution

It refines a least-squares based solution for joint estimation in impulsive biomedical models, offering better accuracy and practical usability.

Findings

Enhanced estimation accuracy on synthetic data

Improved ease of use over previous algorithms

Refined least-squares formulation for impulsive models

Abstract

A hybrid model for biomedical time series comprising a continuous second-order linear time-invariant system driven by an input sequence of positively weighted Dirac delta-functions is considered. The problem of the joint estimation of the input sequence and the continuous system parameters from output measurements is investigated. A solution that builds upon and refines a previously published least-squares formulation is proposed. Based on a thorough analysis of the properties of the least-squares solution, improvements in terms of accuracy and ease of use are achieved on synthetic data, compared to the original algorithm.