This 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 provide 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, heartrate 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.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
17
17 farbige Abbildungen
XV, 228 p. 17 illus. in color.
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 19 mm
Gewicht
ISBN-13
978-3-031-47103-2 (9783031471032)
DOI
10.1007/978-3-031-47104-9
Schweitzer Klassifikation
Dilranjan S. Wickramasuriya received his B.S. degree in electronic and telecommunication engineering from the University of Moratuwa, in 2014, and the M.S. degree in electrical engineering from the University of South Florida, in 2017. In 2020, he completed his Ph.D. degree in electrical engineering at the University of Houston. He works as a Software Engineer at hSenid Mobile Solutions.
Rose T. Faghih is an associate professor of Biomedical Engineering at the New York University (NYU) where she directs the Computational Medicine Laboratory. She received a bachelor's degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from Massachusetts Institute of Technology (MIT), where she was a member of the MIT Laboratory for Information and Decision Systems as well as the MIT-Harvard Neuroscience Statistics Research Laboratory. She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Dr. Faghih is the recipient of various awards including a 2023 National Institutes of Health (NIH) Maximizing Investigators' Research Award for Early Stage Investigators, a 2020 National Science Foundation CAREER Award, a 2020 MIT Technology Review Innovator Under 35 award, and a 2016 IEEE-USA New Face of Engineering award. In 2020, she was featured by the IEEE Women in Engineering Magazine as a "Woman to Watch". Moreover, she was selected by the National Academy of Engineering for the 2019 US and the 2023 EU-US Frontiers of Engineering Programs. Dr. Faghih is on the editorial board of PNAS Nexus by the National Academy of Sciences and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Her research interests include wearable technologies, medical cyber-physical systems, neural and biomedical signal processing, as well as control, estimation, and system identification of biomedical and neural systems.
Introduction.- 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.
