2014
DOI: 10.1371/journal.pone.0085966
|View full text |Cite
|
Sign up to set email alerts
|

Computer-Assisted Interpretation of the EEG Background Pattern: A Clinical Evaluation

Abstract: ObjectiveInterpretation of the EEG background pattern in routine recordings is an important part of clinical reviews. We evaluated the feasibility of an automated analysis system to assist reviewers with evaluation of the general properties in the EEG background pattern.MethodsQuantitative EEG methods were used to describe the following five background properties: posterior dominant rhythm frequency and reactivity, anterior-posterior gradients, presence of diffuse slow-wave activity and asymmetry. Software run… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
6
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
4
4
1
1

Relationship

2
8

Authors

Journals

citations
Cited by 19 publications
(7 citation statements)
references
References 37 publications
0
6
0
Order By: Relevance
“…To avoid any possible influences of the digital filters on the recorded signal, it is therefore important to consider elimination of software based filtering or to use zero-phase filtering, for example by a forward and reversed filtering approach. (Lodder et al , 2014). The current complexity and limited transparency of automated detection systems demotivate researchers to use it on a larger scale (Anderson et al , 2010) although recent advances are promising (Shibasaki et al , 2014).…”
Section: Artifact Handling and Filteringmentioning
confidence: 99%
“…To avoid any possible influences of the digital filters on the recorded signal, it is therefore important to consider elimination of software based filtering or to use zero-phase filtering, for example by a forward and reversed filtering approach. (Lodder et al , 2014). The current complexity and limited transparency of automated detection systems demotivate researchers to use it on a larger scale (Anderson et al , 2010) although recent advances are promising (Shibasaki et al , 2014).…”
Section: Artifact Handling and Filteringmentioning
confidence: 99%
“…For several decades, traditional machine learning techniques have been frequently applied to brain imaging data, including electroencephalography (EEG), with applications ranging from characterization of the EEG background pattern 18 , 19 or quantification of focal or global ischaemia 20 22 to detection of epileptiform discharges 23 , 24 and diagnostics in depression 16 . Common to most of these techniques is the requirement for prior assumptions to guide extraction of particular features to be used for classification 25 .…”
Section: Introductionmentioning
confidence: 99%
“…In this context, it will assume a very important role the possibility to develop some kind of semi-automated analysis with the aim to help clinicians and researchers during these very crucial steps. Few recent studies used computer-assisted tools to allow interpreting EEG background patterns [7,8] but, probably, as suggested by van Diessen et. al.…”
Section: Introductionmentioning
confidence: 99%