چکیده (انگلیسی):

One of the major challenges in the medical domain today is howto exploit the huge amount of data that this field
generates. To do this, approaches are required that are capable of discovering knowledge that is useful for
decision making in the medical field. Time series are data types that are common in the medical domain and
require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated
within particular time series regions, known as events.
This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to
discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic
(200) patient electronic health records (EHR). The view offered in the paper is based on the experience
gathered as part of the VIIP project.1
Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by
illustrating the application of several techniques that cover the entire KDD process through two case studies.
This paper illustrates the application of different knowledge discovery techniques for the purposes of classification
within the above domains. The accuracy of this application for the two classes considered in each case is
99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1%
for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results
than other traditional neural network-based classification techniques.