LEADER 04320nam 22006975 450 001 9910845077403321 005 20240329202130.0 010 $a3-031-47104-0 024 7 $a10.1007/978-3-031-47104-9 035 $a(CKB)31252887400041 035 $a(MiAaPQ)EBC31281836 035 $a(Au-PeEL)EBL31281836 035 $a(DE-He213)978-3-031-47104-9 035 $a(OCoLC)1428780971 035 $a(EXLCZ)9931252887400041 100 $a20240329d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Filter Design for Computational Medicine $eA State-Space Estimation Framework /$fby Dilranjan S. Wickramasuriya, Rose T. Faghih 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (235 pages) 311 $a3-031-47103-2 327 $aIntroduction -- Some Useful Statistical Results -- State-space Model with One Binary Observation -- State-space Model with One Binary and One Continuous Observation -- State-space Model with One Binary and Two Continuous Observations -- State-space Model with One Binary, Two Continuous and a Spiking-type Observation -- State-space Model with One Marked Point Process (MPP) Observation -- Additional Models and Derivations -- MATLAB Code Examples -- List of Supplementary MATLAB Functions. 330 $aThis book serves as a tutorial that explains how different state estimators (Bayesian filters) can be built when all or part of the observations are binary. The book begins by briefly motivating the need for point process state estimation followed by an introduction to the overall approach, as well as some basic background material in statistics that are necessary for the equation derivations that are utilized in subsequent chapters. The subsequent chapters focus on different state-space models and provides step-by-step explanations on how to build the corresponding Bayesian filters. Each of the main chapters that describes a single state-space model also describes the corresponding MATLAB code examples at the end. Descriptions are also provided regarding the code. The code contains both simulated and experimental data examples. All the experimental data examples are taken from real-world experiments. The experiments involve the recording of skin conductance, heart rate and blood cortisol data. A MATLAB toolbox of code examples that cover the different filters covered in the book is included in a companion webpage. The book is primarily intended for graduate students in either electrical engineering or biomedical engineering who will be beginning research in state estimation related to point process data or mixed data (i.e., point processes and other types of observations). The book can also be used by practicing researchers who measure skin conductance and heart rate or pulsatile hormones in their own work (e.g. in psychology). This is an open access book. 606 $aComputational neuroscience 606 $aNeurotechnology (Bioengineering) 606 $aBiomedical engineering 606 $aSignal processing 606 $aBiophysics 606 $aCell interaction 606 $aComputational Neuroscience 606 $aNeuroengineering 606 $aBiomedical Engineering and Bioengineering 606 $aDigital and Analog Signal Processing 606 $aMechanobiological Cell Signaling 615 0$aComputational neuroscience. 615 0$aNeurotechnology (Bioengineering). 615 0$aBiomedical engineering. 615 0$aSignal processing. 615 0$aBiophysics. 615 0$aCell interaction. 615 14$aComputational Neuroscience. 615 24$aNeuroengineering. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aDigital and Analog Signal Processing. 615 24$aMechanobiological Cell Signaling. 676 $a612.8 676 $a570.285 700 $aWickramasuriya$b Dilranjan S$01735694 701 $aFaghih$b Rose T$01735695 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910845077403321 996 $aBayesian Filter Design for Computational Medicine$94155010 997 $aUNINA