Literature DB >> 17555950

Development and comparison of four sleep spindle detection methods.

Eero Huupponen1, Germán Gómez-Herrero, Antti Saastamoinen, Alpo Värri, Joel Hasan, Sari-Leena Himanen.   

Abstract

OBJECTIVE: The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS: All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored bilateral spindles occurring in frontopolar or central brain location. In the present work a new sigma index for spindle detection was developed, based on the fast Fourier transform (FFT) spectrum, aiming at approximating our previous fuzzy spindle detector. The sigma index was complemented with spindle amplitude analysis, based on finite impulse response (FIR) filtering, to form of a combination detector of bilateral spindles. In this combination detector, the spindle amplitude distribution of each recording was estimated and used to tune two different amplitude thresholds. This combination detector was compared to bilaterally extracted sigma indexes and fuzzy detections, which aim to be independent of absolute spindle amplitudes. As a fourth method a fixed spindle amplitude detector was included.
RESULTS: The combination detector provided the best overall performance; in S2 sleep a 70% true positive rate was reached with a specificity of 98.6%, and a false-positive rate of 32%. The bilateral sigma indexes provided the second best results, followed by fuzzy detector, while the fixed amplitude detector provided the poorest results so that in S2 sleep a 70% true positive rate was reached with a specificity of 97.7% and false-positive rate of 46%. The spindle amplitude distributions automatically determined for each recording by the combination detector were compared to amplitudes of visually scored spindles and they proved to correspond well. Inter-hemispheric amplitude variation of visually scored bilateral spindles is also presented.
CONCLUSION: Flexibility is beneficial in the detection of bilateral spindles. The present work advances automated spindle detection and increases the knowledge of bilateral sleep spindle characteristics.

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Mesh:

Year:  2007        PMID: 17555950     DOI: 10.1016/j.artmed.2007.04.003

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  23 in total

1.  Inter-expert and intra-expert reliability in sleep spindle scoring.

Authors:  Sabrina L Wendt; Peter Welinder; Helge B D Sorensen; Paul E Peppard; Poul Jennum; Pietro Perona; Emmanuel Mignot; Simon C Warby
Journal:  Clin Neurophysiol       Date:  2014-11-10       Impact factor: 3.708

2.  Enhanced automated sleep spindle detection algorithm based on synchrosqueezing.

Authors:  Muammar M Kabir; Reza Tafreshi; Diane B Boivin; Naim Haddad
Journal:  Med Biol Eng Comput       Date:  2015-03-17       Impact factor: 2.602

3.  Fast and Stable Signal Deconvolution via Compressible State-Space Models.

Authors:  Abbas Kazemipour; Ji Liu; Krystyna Solarana; Daniel A Nagode; Patrick O Kanold; Min Wu; Behtash Babadi
Journal:  IEEE Trans Biomed Eng       Date:  2017-04-13       Impact factor: 4.538

4.  A deep learning approach for real-time detection of sleep spindles.

Authors:  Prathamesh M Kulkarni; Zhengdong Xiao; Eric J Robinson; Apoorva Sagarwal Jami; Jianping Zhang; Haocheng Zhou; Simon E Henin; Anli A Liu; Ricardo S Osorio; Jing Wang; Zhe Chen
Journal:  J Neural Eng       Date:  2019-02-21       Impact factor: 5.379

Review 5.  Spindle Oscillations in Sleep Disorders: A Systematic Review.

Authors:  Oren M Weiner; Thien Thanh Dang-Vu
Journal:  Neural Plast       Date:  2016-03-10       Impact factor: 3.599

6.  Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms.

Authors:  Min-Yin Liu; Adam Huang; Norden E Huang
Journal:  Front Hum Neurosci       Date:  2017-05-18       Impact factor: 3.169

7.  A sleep spindle detection algorithm that emulates human expert spindle scoring.

Authors:  Karine Lacourse; Jacques Delfrate; Julien Beaudry; Paul Peppard; Simon C Warby
Journal:  J Neurosci Methods       Date:  2018-08-11       Impact factor: 2.390

8.  Using a quadratic parameter sinusoid model to characterize the structure of EEG sleep spindles.

Authors:  Abdul J Palliyali; Mohammad N Ahmed; Beena Ahmed
Journal:  Front Hum Neurosci       Date:  2015-05-05       Impact factor: 3.169

9.  Spindles in Svarog: framework and software for parametrization of EEG transients.

Authors:  Piotr J Durka; Urszula Malinowska; Magdalena Zieleniewska; Christian O'Reilly; Piotr T Różański; Jarosław Żygierewicz
Journal:  Front Hum Neurosci       Date:  2015-05-08       Impact factor: 3.169

10.  Pattern recognition with adaptive-thresholds for sleep spindle in high density EEG signals.

Authors:  Jessica Gemignani; Jacopo Agrimi; Enrico Cheli; Angelo Gemignani; Marco Laurino; Paolo Allegrini; Alberto Landi; Danilo Menicucci
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2015
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