title: Data mining in medical time series.
abstract: This article proposes a modular, computer-based methodology to describe and compare medical problems using data mining methods. The methodology focuses on a mathematical formulation of typical classification problems, systematic extraction of interpretable features from time series, and an evaluation adapted to problem-specific preferences and limitations (computational power, interpretability, etc.). The approach is applied to instrumented gait analysis and to the individual design of myoelectric controllers for hand prostheses.
authors.affiliation: Forschungszentrum Karlsruhe GmbH, Institute for Applied Computer Science, Karlsruhe, Germany. ralf.mikut@iai.fzk.de
authors.forename: Ralf
authors.initials: R
authors.lastname: Mikut
authors.forename: Markus
authors.initials: M
authors.lastname: Reischl
authors.forename: Ole
authors.initials: O
authors.lastname: Burmeister
authors.forename: Tobias
authors.initials: T
authors.lastname: Loose
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005684:Gait / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors