Statistical Methods In Epilepsy

Statistical Methods in Epilepsy

Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery. Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods used in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies introductory and advanced statistical methods, providing a practical roadmap that will be invaluable for learners and experts alike. Topics include a primer on version control and coding, pre-processing of imaging and electrophysiological data, hypothesis testing, generalized linear models, survival analysis, network analysis, time-series analysis, spectral analysis, spatial statistics, unsupervised and supervised learning, natural language processing, prospective trial design, pharmacokinetic and pharmacodynamic modeling, and randomized clinical trials. Features: Provides a comprehensive introduction to statistical methods employed in epilepsy research Divided into four parts: Basic Processing Methods for Data Analysis; Statistical Models for Epilepsy Data Types; Machine Learning Methods; and Clinical Studies Covers methodological and practical aspects, as well as worked-out examples with R and Python code provided in the online supplement Includes contributions by experts in the field https://github.com/sharon-chiang/Statistics-Epilepsy-Book/ The handbook targets clinicians, graduate students, medical students, and researchers who seek to conduct quantitative epilepsy research. The topics covered extend broadly to quantitative research in other neurological specialties and provide a valuable reference for the field of neurology.

Computational Intelligence for Signal and Image Processing, volume II

The first volume of this collection comprised 10 research articles that focused on the applications of Computational Intelligence for Signal and Image Processing, such as education, healthcare, and security. The findings presented in this Research Topic showcased the active development and research within the field of Computational Intelligence methods for the times ahead. Due to the success of that first volume and to facilitate its progression, this second volume embarks on an intriguing exploration at the intersection of neuroscience and cutting-edge technology. This edition focuses on algorithms inspired by the intricacies of the brain, delving into how these algorithms act as catalysts for the evolution of methodologies in image/video and signal processing, IoT applications, and beyond. It highlights the profound potential of brain-inspired algorithms to revolutionize various domains, paving the way for innovation and efficiency.

Innovative applications with artificial intelligence methods in neuroimaging data analysis