Jove
Visualize
Contact Us

Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

1.4K
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:
1.4K
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.2K
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
1.2K
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Gut pathogen Clostridium symbiosum rewires macrophage succinylation to drive enteric neuron loss in inflammatory bowel disease.

Cell host & microbe·2026
Same author

Nature-Inspired Meta-Heuristic Algorithms for Detecting Protein Complexes in Protein-Protein Interaction Networks: A Survey.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Expert Consensus on Subxiphoid and Subcostal Arch Thoracoscopic Resection for the Treatment of Thymoma.

Thoracic cancer·2025
Same author

ASPM mediates nuclear entrapment of FOXM1 via liquid-liquid phase separation to promote progression of hepatocarcinoma.

Genome biology·2025
Same author

Epacadostat Overcomes Cetuximab Resistance in Colorectal Cancer by Targeting IDO-Mediated Tryptophan Metabolism.

Cancer science·2025
Same author

Cisplatin Promotes Hepatotoxicity by cGAS-STING Mediated Innate Immune Response.

Journal of gastroenterology and hepatology·2025
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026
Same journal

PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

IEEE journal of biomedical and health informatics·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 17, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.9K

Multi-Channel Fusion Deep Wavelet Spectrum Network for Epileptic Signal Classification.

Jianyong Sun, Zhuo Cao, Jin Zhao

    IEEE Journal of Biomedical and Health Informatics
    |September 18, 2025
    PubMed
    Summary
    This summary is machine-generated.

    MavenNet, a novel Multichannel Wavelet Convolutional Network, enhances epilepsy detection and seizure classification using electroencephalogram (EEG) signals. This advanced deep learning model improves accuracy and interpretability for clinical diagnosis.

    More Related Videos

    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
    09:57

    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

    Published on: September 20, 2024

    3.4K
    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
    10:22

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

    Published on: December 6, 2016

    21.1K

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
    09:32

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    Published on: December 18, 2016

    12.9K
    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
    09:57

    Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

    Published on: September 20, 2024

    3.4K
    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
    10:22

    Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

    Published on: December 6, 2016

    21.1K

    Area of Science:

    • Neuroscience
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Epilepsy detection and seizure classification using electroencephalogram (EEG) signals show progress but face challenges.
    • Existing tensor decomposition methods have high computational demands.
    • Deep learning approaches often neglect the spatial structure inherent in EEG data.

    Purpose of the Study:

    • To introduce MavenNet, a Multichannel Wavelet Convolutional Network, for improved automated epilepsy detection and seizure classification.
    • To address limitations in current EEG signal processing techniques for epilepsy diagnosis.

    Main Methods:

    • MavenNet applies continuous wavelet transform to create a third-order tensor from multichannel EEG data.
    • Multichannel convolution operations process the tensor representation.
    • Class Activation Mapping (CAM) is utilized for model interpretability and feature visualization.

    Main Results:

    • MavenNet demonstrated superior performance compared to leading algorithms across multiple public and private datasets.
    • The model effectively preserves the spatial structure of EEG signals.
    • Enhanced transparency and reliability in classification outcomes were achieved.

    Conclusions:

    • MavenNet offers a valuable advancement for the clinical diagnosis of epilepsy.
    • The model's ability to maintain spatial structure and improve interpretability enhances its utility.
    • This approach represents a significant step forward in automated EEG-based epilepsy analysis.