Additional Models and Derivations-Reference-Cited by-同舟云学术

Additional Models and Derivations

Published:2023-10-13 Issue: Volume: Page:97-109
ISSN:
Container-title:Bayesian Filter Design for Computational Medicine
language:en
Short-container-title:

Author:

Wickramasuriya Dilranjan S.,Faghih Rose T.

Abstract

AbstractMuch of what we have described in the preceding chapters provides the basic tools necessary to build physiological state-space estimators. In this chapter, we will briefly review some additional concepts in state-space estimation, a non-traditional method of estimation, and some supplementary models. These may help serve as pointers if extensions are to be built to the models already described.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-47104-9_9

Reference18 articles.

1. T. P. Coleman, M. Yanike, W. A. Suzuki, and E. N. Brown, “A mixed-filter algorithm for dynamically tracking learning from multiple behavioral and neurophysiological measures,” The Dynamic Brain: An Exploration of Neuronal Variability and its Functional Significance, pp. 3–28, 2011.

2. E. N. Brown, L. M. Frank, D. Tang, M. C. Quirk, and M. A. Wilson, “A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells,” Journal of Neuroscience, vol. 18, no. 18, pp. 7411–7425, 1998.

3. D. S. Wickramasuriya and R. T. Faghih, “A Bayesian filtering approach for tracking arousal from binary and continuous skin conductance features,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 6, pp. 1749–1760, 2020.

4. D. S. Wickramasuriya and R. T. Faghih, “A cortisol-based energy decoder for investigation of fatigue in hypercortisolism,” in 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2019, pp. 11–14.

5. M. M. Shanechi, J. J. Chemali, M. Liberman, K. Solt, and E. N. Brown, “A brain-machine interface for control of medically-induced coma,” PLoS Computational Biology, vol. 9, no. 10, p. e1003284, 2013.