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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:

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Related Experiment Video

Updated: Oct 10, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

A Generalized Linear Model for an ECG-based Neonatal Seizure Detector.

Lorenzo Frassineti, Claudia Manfredi, Benedetta Olmi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 11, 2021

Summary

This study developed an Electrocardiography-based Neonatal Seizure Detector (NSD) using Heart Rate Variability. The ECG-based NSD shows promise for faster seizure detection in newborns, especially when Electroencephalography is unavailable.

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Last Updated: Oct 10, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

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Published on: September 6, 2017

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

Area of Science:

Neonatal Neurology
Biomedical Engineering
Cardiology

Background:

Neonatal seizures are a critical neurological emergency with complex recognition.
Current diagnosis relies on Electroencephalography (EEG), requiring specialized expertise.
There is a need for faster, less invasive seizure detection methods.

Purpose of the Study:

To develop and validate an Electrocardiography (ECG)-based Neonatal Seizure Detector (NSD).
To utilize Heart Rate Variability (HRV) features for seizure detection.
To assess the feasibility of ECG as a simpler diagnostic tool.

Main Methods:

Development of a Generalized Linear Model using HRV features.
Validation on a public dataset of 52 neonatal subjects (33 with seizures, 19 seizure-free).
Evaluation of the detector's performance in identifying seizure events.

Main Results:

The ECG-based NSD achieved a 69% Concatenated Area Under the ROC Curve (AUCcc).
HRV features effectively identified cardio-regulatory alterations during neonatal seizures.
The method showed particular efficacy for seizures related to Hypoxic-Ischaemic Encephalopathies.

Conclusions:

ECG-based NSDs are a viable tool for supporting timely neonatal seizure diagnosis.
This approach offers a practical alternative when EEG is not readily accessible.
The findings support the clinical integration of ECG-based seizure detection in neonates.