Search research articles

Related Concept Videos

Time and frequency -Domain Interpretation of PI Control

Time and frequency -Domain Interpretation of PI Control

Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...

Time and frequency -Domain Interpretation of Phase-lead Control

Time and frequency -Domain Interpretation of Phase-lead Control

Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...

Time and frequency -Domain Interpretation of Phase-lag Control

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...

pre-mRNA Processing

pre-mRNA Processing

In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a “cap” to the 5’ end of the growing transcript. In this process, a 5’ phosphate is replaced by modified guanosine that has a methyl group attached to it (7-Methyl...

Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

Phase Contrast and Differential Interference Contrast Microscopy

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...

You might also read

Related Articles

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

Sort by

Same author

Electroencephalographic Monitoring in the Recovery Room for Identification of Patients at Risk for Postoperative Delirium.

Anesthesiology·2026

Same author

Probabilistic mapping and automated segmentation of human brainstem white matter bundles.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Clinical Electroencephalography for Anesthesiologists and Intensivists: Part 2-Physiologic Signatures and Active Management.

Anesthesiology·2026

Same author

Functional fractionation of large-scale brain networks in the human subcortex.

bioRxiv : the preprint server for biology·2026

Same author

Electroencephalogram signatures of propanidid-mediated sedation and unconsciousness.

Journal of neurophysiology·2025

Same author

Probabilistic Mapping and Automated Segmentation of Human Brainstem White Matter Bundles.

medRxiv : the preprint server for health sciences·2025

Same journal

Complex Indel Detection: A Simulation-Based Framework and Parsing with FreeBayes.

bioRxiv : the preprint server for biology·2026

Same journal

Emulating the gingival-tooth interface during bacterial, fungal, and viral infection in a microphysiological model of the human oral cavity.

bioRxiv : the preprint server for biology·2026

Same journal

Local SNP-explained methylation variation reveals genetically anchored and exposure-associated methylation architecture in the human brain.

bioRxiv : the preprint server for biology·2026

Same journal

Perinatal Semaglutide Treatment Improves Maternal Health and Mitigates Offspring Metabolic Dysfunction in a Mouse Model of Maternal Obesity.

bioRxiv : the preprint server for biology·2026

Same journal

Pervasive cryptic selection in the human noncoding genome.

bioRxiv : the preprint server for biology·2026

Same journal

Secreted ORF8 reprograms macrophages to enhance SARS-CoV-2 infection of lung epithelial cells.

bioRxiv : the preprint server for biology·2026

See all related 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

Search research articles

Home
Time-frequency Embedding With Contrastive Pre-training Allows Sub-second Seizure Detection.

Home
Time-frequency Embedding With Contrastive Pre-training Allows Sub-second Seizure Detection.

Related Experiment Video

Neurocircuit Assays for Seizures in Epilepsy Mutants of Drosophila

Neurocircuit Assays for Seizures in Epilepsy Mutants of Drosophila

Published on: April 15, 2009

Time-frequency embedding with contrastive pre-training allows sub-second seizure detection.

Helena A Merker, Isabella Dalla Betta, Matthew A Wilson

Biorxiv : the Preprint Server for Biology

|February 6, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a 3D convolutional neural network (CNN) with a trainable continuous wavelet transform (CWT) layer for accurate electroencephalogram (EEG) seizure detection. Bidirectional contrastive learning (BiCL) pre-training enhances performance, especially with limited or imbalanced data, enabling sub-second seizure identification.

More Related Videos

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Published on: March 4, 2015

Related Experiment Videos

Neurocircuit Assays for Seizures in Epilepsy Mutants of Drosophila

Neurocircuit Assays for Seizures in Epilepsy Mutants of Drosophila

Published on: April 15, 2009

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Published on: March 4, 2015

Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Accurate electroencephalogram (EEG) seizure detection is crucial for clinical diagnosis and research.
Time-frequency domain analysis offers richer insights into seizure dynamics than traditional time-domain methods.
Existing methods face challenges with data limitations like noise, downsampling, and class imbalance.

Purpose of the Study:

To develop and evaluate a novel 3D convolutional neural network (CNN) with an integrated trainable continuous wavelet transform (CWT) layer for adaptive time-frequency feature learning from raw EEG.
To investigate the efficacy of self-supervised pre-training strategies, specifically contrastive predictive coding (CPC) and bidirectional contrastive learning (BiCL), to enhance CNN performance.
To assess the framework's robustness against common data challenges, including low data availability, class imbalance, noise, downsampling, and cross-subject generalization.

Main Methods:

A 3D CNN architecture was designed, incorporating a trainable CWT layer for direct time-frequency feature extraction from EEG signals.
Contrastive learning techniques, CPC and BiCL, were employed for pre-training the 3D CNN to improve feature representation.
Performance was evaluated on single-channel and multi-channel EEG data, comparing against 2D CNN and 1D CNN models, and tested under various data-degrading conditions.

Main Results:

The proposed 3D CNN with a trainable CWT layer achieved over 95% accuracy for seizure detection in segments as short as 0.5 seconds.
The 3D CNN with BiCL pre-training demonstrated superior performance, particularly in low-data and class-imbalanced scenarios, outperforming the standard 3D CNN.
The model maintained high accuracy (>90%) even with moderate noise, downsampling, and when generalizing to unseen subjects, indicating robustness.

Conclusions:

A 3D CNN framework with a trainable CWT layer and BiCL pre-training enables highly accurate, sub-second electroencephalogram seizure detection.
This approach effectively addresses practical data limitations encountered in clinical settings, offering a robust solution.
Integrating time-frequency embedding within CNNs, augmented by self-supervised pre-training, presents a promising direction for advanced seizure detection systems.