Statistical Methods in Epilepsy (Original PDF from Publisher)

Statistical Methods in Epilepsy (Original PDF from Publisher)

Written by Sharon Chiang

Research on epilepsy holds the potential to provide novel treatments and understanding of brain activity, but the key to deriving significance from data and

facilitating exploration is machine learning and statistics.

A thorough introduction to statistical techniques used in epilepsy research may be found in Statistical Methods in Epilepsy.

Leading authorities have written this textbook in an understandable, approachable way that demystifies both basic and sophisticated statistical techniques,

offering a useful road map that will be beneficial to both novices and experts.

Version control and coding basics, pre-processing of electrophysiological and imaging data, hypothesis testing, generalized linear models, survival analysis,

network analysis, time-series analysis, spectral analysis, spatial statistics, natural language processing, prospective trial design, pharmacokinetic and

pharmacodynamic modeling and randomized clinical trials are among the topics covered.

Qualities :

gives a thorough overview of the statistical techniques used in epilepsy research.

separated into four sections : Methods of Machine Learning, Statistical Models for Epilepsy Data Types, Clinical Studies and Basic Processing Methods for Data

Analysis includes worked-out examples with R and Python code provided in the online supplement, covering methodological and practical elements.

Includes additions by subject-matter specialists at https://github.com/sharon-chiang/Statistics-Epilepsy-Book/

The manual is intended for academics wishing to undertake quantitative epilepsy research as well as physicians, graduate students and clinicians.

The subjects addressed serve as a useful reference for the study of neurology and are broadly applicable to quantitative research in other neurological disciplines.