Sleep spindle detection using multivariate Gaussian mixture models

被引：10

作者：

Patti, Chanakya Reddy ^{[1
]}

Penzel, Thomas ^{[2
,3
]}

Cvetkovic, Dean ^{[1
]}

机构：

[1] RMIT Univ, Sch Engn, Melbourne, Vic 3083, Australia

[2] Charite Univ Med Berlin, Interdisciplinary Sleep Ctr, Berlin, Germany

[3] St Annes Univ Hosp Brno, Int Clin Res Ctr, Brno, Czech Republic

来源：

JOURNAL OF SLEEP RESEARCH | 2018年 / 27卷 / 04期

关键词：

Sigma index; expectation maximization; infinite impulse response filters; EEG; BENCHMARKING; RECOGNITION; TIME;

D O I：

10.1111/jsr.12614

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

In this research study we have developed a clustering-based automatic sleep spindle detection method that was evaluated on two different databases. The databases consisted of 20 all-night polysomnograph recordings. Past detection methods have been based on subject-independent and some subject-dependent parameters, such as fixed or variable thresholds to identify spindles. Using a multivariate Gaussian mixture model clustering technique, our algorithm was developed to use only subject-specific parameters to detect spindles. We have obtained an overall sensitivity range (65.1-74.1%) at a (59.55-119.7%) false positive proportion.

引用

页数：12

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