Signal regularity-based automated seizure detection system for scalp EEG monitoring 1

被引：6

作者：

Shiau D.-S. ^{[1
]}

Halford J.J. ^{[2
]}

Kelly K.M. ^{[3
,4
,5
]}

Kern R.T. ^{[1
]}

Inman M. ^{[1
]}

Chien J.-H. ^{[6
,7
]}

Pardalos P.M. ^{[8
]}

Yang M.C.K. ^{[9
]}

Sackellares J.Ch. ^{[1
]}

机构：

[1] Optima Neuroscience, Inc., Gainesville, FL

[2] Medical University of South Carolina, Charleston, SC

[3] Drexel University College of Medicine, Philadelphia, PA

[4] Allegheny General Hospital, Pittsburgh, PA

[5] Allegheny-Singer Research Institute, Pittsburgh, PA

[6] Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL

[7] Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL

[8] Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL

[9] Department of Statistics, University of Florida, Gainesville, FL

来源：

Cybernetics and Systems Analysis | 2010年 / 46卷 / 06期

关键词：

Amplitude variation; Artifact rejection; False detection rate; Local maximum frequency; Pattern match regularity statistic (PMRS); Scalp EEG; Seizure detection; Sensitivity;

D O I：

10.1007/s10559-010-9273-3

中图分类号：

学科分类号：

摘要：

The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset. © 2010 Springer Science+Business Media, Inc.

引用

页码：922 / 935

页数：13

共 21 条

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