Automatic Sleep Spindles Identification and Classification with Multitapers and Convolution

Ignacio A Zapata; Peng Wen; Evan Jones; Shauna Fjaagesund; Yan Li

doi:10.1093/sleep/zsad159

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Automatic Sleep Spindles Identification and Classification with Multitapers and Convolution

Journal article

Open access

Peer reviewed

Automatic Sleep Spindles Identification and Classification with Multitapers and Convolution

Ignacio A Zapata, Peng Wen, Evan Jones, Shauna Fjaagesund and Yan Li

Sleep, Vol.47(1), pp.1-11

2024

DOI: https://doi.org/10.1093/sleep/zsad159

PMCID: PMC10782498

PMID: 37294908

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Abstract

Multitapers

spectral estimation

sleep EEG

sleep spindles

spectra density estimation (SDE)

Sleep spindles are isolated transient surges of oscillatory neural activity present during sleep stages 2 and 3 in the non-rapid eye movement (NREM). They can indicate the mechanisms of memory consolidation and plasticity in the brain. Spindles can be identified across cortical areas and classified as either slow or fast. There are spindle transients across different frequencies and power, yet most of their functions remain a mystery. Using several electroencephalogram (EEG) databases, this study presents a new method, called the “spindles across multiple channels” (SAMC) method, for identifying and categorising sleep spindles in EEGs during the NREM sleep. The SAMC method uses a multitapers and convolution (MT&C) approach to extract the spectral estimation of different frequencies present in sleep EEGs and graphically identify spindles across multiple channels. The characteristics of spindles, such as duration, power, and event areas, are also extracted by the SAMC method. Comparison with other state-of-the-art spindle identification methods demonstrated the superiority of the proposed method with an agreement rate, average positive predictive value, and sensitivity of over 90% for spindle classification across the three databases used in this paper. The computing cost was found to be, on average, 0.004 seconds per epoch. The proposed method can potentially improve the understanding of the behaviour of spindles across the scalp and accurately identify and categorise sleep spindles.

Details

Title: Automatic Sleep Spindles Identification and Classification with Multitapers and Convolution
Authors: Ignacio A Zapata (Corresponding Author) - University of Southern Queensland
Peng Wen (Author)
Evan Jones (Author)
Shauna Fjaagesund (Author) - University of the Sunshine Coast, Queensland, School of Health and Sport Sciences - Legacy
Yan Li (Author) - University of Southern Queensland
Publication details: Sleep, Vol.47(1), pp.1-11
Publisher: Oxford University Press
Date published: 2024
DOI: 10.1093/sleep/zsad159
ISSN: 1550-9109
PMID: 37294908; PMC10782498
Copyright note: This is a pre-copyedited, author-produced version of an article accepted for publication in SLEEP following peer review. The version of record Ignacio A Zapata and others, Automatic Sleep Spindles Identification and Classification with Multitapers and Convolution, Sleep, 2023;, zsad159, https://doi.org/10.1093/sleep/zsad159 is available online at: https://doi.org/10.1093/sleep/zsad159.
Organisation Unit: University of the Sunshine Coast, Queensland; School of Health and Behavioural Sciences - Legacy
Language: English
Record Identifier: 99735298702621
Output Type: Journal article

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