Jove
Visualize
Contact Us
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 Concept Videos

Seizures: Classification01:13

Seizures: Classification

2.5K
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:
2.5K
Seizures l: Introduction01:20

Seizures l: Introduction

36
Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...
36
Seizures ll: Types01:19

Seizures ll: Types

25
Seizures are sudden bursts of abnormal electrical discharge in the brain that interfere with normal function. They are commonly divided into three groups: focal seizures, generalized seizures, and other types that do not fit neatly into either category.Focal SeizuresFocal seizures begin in a single brain region. When awareness is preserved, they are called focal aware seizures and may cause sensations such as tingling, unusual smells, or flashing lights. When awareness is impaired, they are...
25
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.7K
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...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Dual-representation structural MRI classification of psychiatric disorders using deep learning and large language models.

Psychiatry research. Neuroimaging·2026
Same author

Data fusion of medical imaging in neurological disorders.

Reviews in the neurosciences·2025
Same author

Image-based food groups and portion prediction by using deep learning.

Journal of food science·2025
Same author

Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.

Journal of medical systems·2024
Same author

Illiterate Addenbrooke's Cognitive Examination-III in Three Indian Languages: An Adaptation and Validation Study.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2024
Same author

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning.

Journal of medical systems·2024

Related Experiment Video

Updated: Apr 28, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

8.9K

A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection.

Priyanka Sharma1, Yusuf Uzzaman Khan1, Omar Farooq2

  • 1Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India.

Clinical EEG and Neuroscience
|June 18, 2014
PubMed
Summary

This study introduces a novel wavelet-based method for accurately detecting nonconvulsive seizures (NCSz) using electroencephalography (EEG) signals. The approach successfully identified all seizures in a patient database with a low false positive rate.

Keywords:
cubical thresholdingdispersion measuresnonconvulsive seizure detectionwavelet denoising

More Related Videos

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

13.7K
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

22.8K

Related Experiment Videos

Last Updated: Apr 28, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

8.9K
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

13.7K
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

22.8K

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Nonconvulsive seizures (NCSz) pose diagnostic challenges due to subtle or absent physical symptoms.
  • Automatic seizure detection methods exist, but focus on NCSz remains limited.
  • Electroencephalography (EEG) signals are prone to nonstationary noise, necessitating effective denoising.

Purpose of the Study:

  • To propose a reliable method for the automatic detection of nonconvulsive seizures (NCSz).
  • To develop and evaluate a novel wavelet-based denoising technique for EEG signals.
  • To discriminate between normal and seizure EEGs using extracted statistical features.

Main Methods:

  • A wavelet-based denoising approach utilizing cubical thresholding was applied to EEG signals.
  • Three statistical features were extracted from specific wavelet frequency bands (0-8, 8-16, 16-32, and 0-32 Hz).
  • A linear classifier was trained using these features to differentiate between normal and seizure EEG data.

Main Results:

  • The proposed method achieved successful detection of all recorded nonconvulsive seizures.
  • The system demonstrated a low false positive rate of 0.7 per hour.
  • The wavelet-based denoising effectively reduced noise in the EEG signals prior to analysis.

Conclusions:

  • The developed wavelet-based denoising and feature extraction method provides a reliable approach for detecting nonconvulsive seizures (NCSz).
  • This technique holds promise for improving the diagnosis and management of conditions involving NCSz.
  • The study highlights the importance of signal preprocessing in accurate EEG analysis for seizure detection.