Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms

Olivier Pallanca; Sammy Khalife; Jesse Read

doi:10.1109/BIBM.2018.8621305

Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms

Olivier Pallanca
, Sammy Khalife
, Jesse Read

Laboratoire d'Informatique (LIX)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Detection and classification of critical neural events during sleep is a central problem in EEG signal processing. Sleep Spindles constitute the most known pattern and their density in the EEG signal are related to many cerebral functions as memory consolidation, sleep quality or psychiatric diseases. Unfortunately this biomarker is underutilized because human annotation and classification is time consuming and almost impossible to achieve out of the scope of research. There is a need to use a reliable automated approach in order to use this biomarker in clinic.al practice A lot of studies and algorithms already exist and are used to help in this classification, but it remains difficult to achieve a good detection performance, especially when the EEG signal quality is low. We present here a review of the main methods used for spindles patterns detection and we test those where an open-source algorithm is available, to compare precision, recall and the F1-score on our own annotated dataset.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2652-2655
Number of pages	4
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621305
Publication status	Published - 21 Jan 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1109/BIBM.2018.8621305

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Pallanca, O., Khalife, S., & Read, J. (2019). Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2652-2655). Article 8621305 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621305

Pallanca, Olivier ; Khalife, Sammy ; Read, Jesse. / Detection of sleep spindles in NREM 2 sleep stages : Preliminary study benchmarking of algorithms. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2652-2655 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{fa3c78cb62ff42e8a999914c1cb574a5,

title = "Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms",

abstract = "Detection and classification of critical neural events during sleep is a central problem in EEG signal processing. Sleep Spindles constitute the most known pattern and their density in the EEG signal are related to many cerebral functions as memory consolidation, sleep quality or psychiatric diseases. Unfortunately this biomarker is underutilized because human annotation and classification is time consuming and almost impossible to achieve out of the scope of research. There is a need to use a reliable automated approach in order to use this biomarker in clinic.al practice A lot of studies and algorithms already exist and are used to help in this classification, but it remains difficult to achieve a good detection performance, especially when the EEG signal quality is low. We present here a review of the main methods used for spindles patterns detection and we test those where an open-source algorithm is available, to compare precision, recall and the F1-score on our own annotated dataset.",

author = "Olivier Pallanca and Sammy Khalife and Jesse Read",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621305",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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Pallanca, O, Khalife, S & Read, J 2019, Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621305, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2652-2655, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621305

Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms. / Pallanca, Olivier; Khalife, Sammy; Read, Jesse.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2652-2655 8621305 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of sleep spindles in NREM 2 sleep stages

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

AU - Pallanca, Olivier

AU - Khalife, Sammy

AU - Read, Jesse

PY - 2019/1/21

Y1 - 2019/1/21

N2 - Detection and classification of critical neural events during sleep is a central problem in EEG signal processing. Sleep Spindles constitute the most known pattern and their density in the EEG signal are related to many cerebral functions as memory consolidation, sleep quality or psychiatric diseases. Unfortunately this biomarker is underutilized because human annotation and classification is time consuming and almost impossible to achieve out of the scope of research. There is a need to use a reliable automated approach in order to use this biomarker in clinic.al practice A lot of studies and algorithms already exist and are used to help in this classification, but it remains difficult to achieve a good detection performance, especially when the EEG signal quality is low. We present here a review of the main methods used for spindles patterns detection and we test those where an open-source algorithm is available, to compare precision, recall and the F1-score on our own annotated dataset.

AB - Detection and classification of critical neural events during sleep is a central problem in EEG signal processing. Sleep Spindles constitute the most known pattern and their density in the EEG signal are related to many cerebral functions as memory consolidation, sleep quality or psychiatric diseases. Unfortunately this biomarker is underutilized because human annotation and classification is time consuming and almost impossible to achieve out of the scope of research. There is a need to use a reliable automated approach in order to use this biomarker in clinic.al practice A lot of studies and algorithms already exist and are used to help in this classification, but it remains difficult to achieve a good detection performance, especially when the EEG signal quality is low. We present here a review of the main methods used for spindles patterns detection and we test those where an open-source algorithm is available, to compare precision, recall and the F1-score on our own annotated dataset.

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U2 - 10.1109/BIBM.2018.8621305

DO - 10.1109/BIBM.2018.8621305

M3 - Conference contribution

AN - SCOPUS:85062487871

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 2652

EP - 2655

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 3 December 2018 through 6 December 2018

ER -

Pallanca O, Khalife S, Read J. Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2652-2655. 8621305. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621305

Detection of sleep spindles in NREM 2 sleep stages: Preliminary study benchmarking of algorithms

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