Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Stages of Sleep

Stages of Sleep

Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...

Sleep-Wake Cycles

Sleep-Wake Cycles

Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Gamer in the scanner: Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Trait and state predictors of the intensity of emotions experienced in everyday dreams: a multilevel approach.

Scientific reports·2026

Same author

Investigating the relationship between sleep habits and the frequency and expression of hallucinatory-like experiences in the French general population.

Sleep medicine·2026

Same author

Using virtual reality hypnosis during stem cell transplant for patients in hematology: A protocol for a feasibility randomized study.

PloS one·2026

Same author

Divergent creativity in humans and large language models.

Scientific reports·2026

Same author

Comparing the characteristics of hallucinations and mental imagery: a large cross-sectional study in the general population.

Consciousness and cognition·2025

Same journal

The causal efficacy of consciousness: a neuroscientific analysis and explanation.

Frontiers in human neuroscience·2026

Same journal

Temporal-oscillatory entrainment: a multi-timescale framework for rhythmic coordination from neural to social frequencies.

Frontiers in human neuroscience·2026

Same journal

Role of AQP4 in ameliorating heat stress-induced cellular injury in a cell line model through active heat acclimation.

Frontiers in human neuroscience·2026

Same journal

Correction: Cognitive state monitoring for neuroadaptive information visualization.

Frontiers in human neuroscience·2026

Same journal

The synthetic self-hypothesis: dopaminergic redirection through self-face recognition in stuttering therapy.

Frontiers in human neuroscience·2026

Same journal

A randomised, placebo-controlled, triple-blind clinical trial to investigate the efficacy of <i>Ginkgo biloba</i> extract EGb 761<sup>®</sup> in cognitive impairment associated with post COVID-19 syndrome-the EGb COCOS protocol.

Frontiers in human neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 5, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis.

Tarek Lajnef¹, Sahbi Chaibi¹, Jean-Baptiste Eichenlaub²

¹LETI Lab, Sfax National Engineering School, University of Sfax Sfax, Tunisia.

Frontiers in Human Neuroscience

|August 19, 2015

Summary

This summary is machine-generated.

A new method using discrete tunable Q-factor wavelet transform (TQWT) and morphological component analysis (MCA) accurately detects sleep spindles and K-complexes. This framework offers a promising alternative for automated sleep stage analysis.

Keywords:

K-complex automatic detection electroencephalography (EEG)morphological component analysis (MCA)neural oscillations sleep spindles tunable Q-factor wavelet transform (TQWT)

More Related Videos

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Related Experiment Videos

Last Updated: Apr 5, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Area of Science:

Neuroscience
Signal Processing
Sleep Medicine

Background:

Sleep stage S2 is characterized by sleep spindles and K-complexes.
Accurate detection of these events is crucial for sleep analysis.
Existing automated methods may require improvement in sensitivity and specificity.

Purpose of the Study:

To propose a novel framework for the joint detection of sleep spindles and K-complexes.
To utilize discrete tunable Q-factor wavelet transform (TQWT) and morphological component analysis (MCA) for signal decomposition.
To evaluate the performance of the proposed method against visual scoring and alternative pipelines.

Main Methods:

Sleep electroencephalography (EEG) signals were decomposed into oscillatory (spindles) and transient (K-complex) components using TQWT and MCA.
Detection was achieved by thresholding the decomposed components.
Optimal thresholds were determined using ROC-like curves (sensitivity vs. FDR).

Main Results:

The proposed TQWT-MCA method achieved 83.18% sensitivity for spindles and 81.57% for K-complexes, with FDRs of 39% and 29.54%, respectively.
Excluding TQWT and MCA significantly reduced detection sensitivity and increased FDR.
Performance was validated on both collected and publicly available sleep EEG data.

Conclusions:

The TQWT-MCA framework provides a robust and effective method for automated detection of sleep spindles and K-complexes.
This approach demonstrates potential as a valuable alternative to current detection techniques.
Further validation on large-scale datasets is recommended for broader application.