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

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:
Seizures l: Introduction01:20

Seizures l: Introduction

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,...
Seizures ll: Types01:19

Seizures ll: Types

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...
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

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...
Classification of Signals01:30

Classification of Signals

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...

You might also read

Related Articles

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

Sort by
Same author

Long-Term, Patient-Specific Seizure Prediction using Absolute Mean Instantaneous Frequency Difference (AMIFD) and Seizure Clustering.

IEEE journal of biomedical and health informatics·2026
Same author

Seizure onset zone (SoZ) identification from interictal intracranial electroencephalogram.

Scientific reports·2025
Same author

Patient-specific long-term seizure prediction via multi-model classification.

Journal of neural engineering·2025
Same author

Prediction of Clinical Response of Transcranial Magnetic Stimulation Treatment for Major Depressive Disorder Using Hyperdimensional Computing.

IEEE journal of biomedical and health informatics·2025
Same author

MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

Computers in biology and medicine·2025
Same author

Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks.

Journal of neural engineering·2024

Related Experiment Videos

Low complexity algorithm for seizure prediction using Adaboost.

Manohar Ayinala1, Keshab K Parhi

  • 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. ayina004@umn.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study introduces a new, low-complexity algorithm for patient-specific seizure prediction using electroencephalogram (EEG) data. The developed method achieves high accuracy and a low false alarm rate, making it suitable for implantable devices.

Related Experiment Videos

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Epilepsy affects millions globally, necessitating accurate seizure prediction.
  • Existing seizure prediction algorithms often face challenges with complexity and real-time application.
  • Patient-specific approaches are crucial for personalized epilepsy management.

Purpose of the Study:

  • To develop a novel, low-complexity, patient-specific algorithm for predicting seizure events.
  • To enhance the efficiency and applicability of seizure prediction for clinical use.
  • To investigate the use of Adaboost for feature selection and classification in EEG analysis.

Main Methods:

  • Utilized the Adaboost algorithm for both feature selection and classification stages.
  • Extracted spectral power features from electroencephalogram (EEG) recordings across 9 sub-bands.
  • Developed a new feature ranking method and employed a non-linear classifier using decision stumps.

Main Results:

  • Achieved a high sensitivity of 94.375% for 71 seizure events.
  • Reported a low false alarm rate of 0.13 per hour.
  • Demonstrated suitability for implantable devices due to low computational complexity, using an average of 5 features.

Conclusions:

  • The proposed patient-specific algorithm offers a promising solution for accurate and efficient seizure prediction.
  • The low computational demands make it ideal for integration into wearable or implantable epilepsy monitoring devices.
  • This approach advances the potential for real-time seizure forecasting and improved patient care.