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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:

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...

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Related Experiment Video

Updated: Jun 21, 2026

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

Newborn seizure detection based on heart rate variability.

M B Malarvili¹, Mostefa Mesbah

¹Faculty of Biomedical and Health Science Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia. malarvili@utm.my

IEEE Transactions on Bio-Medical Engineering

|July 25, 2009

Summary

Heart rate variability (HRV) can detect newborn seizures automatically. This method analyzes ECG data, extracts key HRV features, and uses a classifier, achieving high accuracy in neonatal seizure detection.

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Last Updated: Jun 21, 2026

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How to Obtain Reliable Visual Event-related Potentials in Newborns

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Published on: October 24, 2019

Area of Science:

Biomedical Engineering
Neonatal Neurology
Cardiology

Background:

Neonatal seizures are a critical concern requiring accurate detection methods.
Electrocardiogram (ECG) signals offer a potential source for monitoring neonatal health.
Heart rate variability (HRV) reflects autonomic nervous system function, which may be altered during seizures.

Purpose of the Study:

To investigate the efficacy of heart rate variability (HRV) for automatic seizure detection in newborns.
To develop and validate an HRV-based algorithm for distinguishing seizure from non-seizure states in neonates.

Main Methods:

Obtaining HRV from neonatal ECG recordings.
Extracting time-frequency HRV features due to signal non-stationarity.
Employing a two-phase wrapper-based feature selection for optimal subset identification.
Classifying HRV features using a supervised statistical classifier for seizure detection.

Main Results:

The proposed HRV-based algorithm achieved 85.7% sensitivity for seizure detection.
The algorithm demonstrated 84.6% specificity in differentiating seizure from non-seizure states.
Feature selection identified a minimal yet highly discriminative subset of HRV features.

Conclusions:

HRV is sensitive to cardioregulatory system changes induced by neonatal seizures.
HRV analysis provides a viable basis for developing automatic neonatal seizure detection systems.
The proposed method shows promise for non-invasive, real-time seizure monitoring in newborns.