Autoregressive models for biomedical signal processing

• Published in: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2023; pp. 1 - 6
• Main Authors: Haderlein, Jonas F., Peterson, Andre D. H., Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2023
• Subjects: Algorithms; Biological system modeling; Biomedical Engineering; Brain; Brain modeling; Computational modeling; Signal processing; Signal Processing, Computer-Assisted; Time Factors; Time series analysis; Uncertainty
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10340714

Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters.Clinical relevance- This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy.