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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:

Epilepsy and Seizures: Overview

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...

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Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

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Seizure detection using wearable sensors and machine learning: Setting a benchmark.

Jianbin Tang¹, Rima El Atrache², Shuang Yu¹

¹IBM Research Australia, Melbourne, Victoria, Australia.

|July 16, 2021

Summary

Machine learning algorithms using wearable biosensors can detect a wide range of epileptic seizures. This technology shows promise for improving epilepsy monitoring beyond traditional caretaker diaries.

Keywords:

deep learning epilepsy machine learning multisensor recordings wearable devices

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Last Updated: Oct 28, 2025

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Published on: June 21, 2019

Area of Science:

Neurology
Biomedical Engineering
Machine Learning

Background:

Epilepsy monitoring traditionally relies on caretaker seizure diaries, which can be incomplete.
Wearable devices offer a more suitable and tolerable approach for long-term ambulatory seizure monitoring.

Purpose of the Study:

To evaluate the seizure detection performance of custom-developed machine learning (ML) algorithms.
To assess these algorithms using wrist- and ankle-worn multisignal biosensors across various epileptic seizure types.

Main Methods:

Enrolled patients in an epilepsy monitoring unit wearing wrist or ankle sensors.
Collected data on body temperature, electrodermal activity, accelerometry (ACC), and photoplethysmography (BVP).
Trained and validated two ML algorithms: seizure type-specific and seizure type-agnostic detection models, using electroencephalography (EEG) as the gold standard.

Main Results:

Included 94 patients and 548 epileptic seizures across nine seizure types.
Both ML algorithms demonstrated better-than-chance detection for all seizure types (AUC-ROC .642-.995).
A fusion of ACC and BVP modalities yielded the best AUC-ROC of .752 for general seizure detection.

Conclusions:

Feasible automatic seizure detection using ML from multimodal wearable sensor data across diverse epileptic seizures.
Preliminary results indicate promising, better-than-chance seizure detection capabilities.
Future work includes validation on larger datasets and integration of additional clinical information.