AI analyzing EEG waveforms to detect seizures.

Decoding Seizures: How AI and Brainwave Analysis Are Changing Epilepsy Detection

Avery Sinclair in Health & Wellness November 2025 • 4 min read.

"AI-powered EEG analysis offers a transformative approach to detecting epileptic seizures, providing new hope for faster and more accurate diagnoses."

Epilepsy, a chronic neurological disorder characterized by recurrent seizures, affects millions worldwide. These seizures, resulting from abnormal electrical activity in the brain, can significantly impair a person's quality of life, impacting everything from their physical safety to their cognitive functions. The unpredictable nature of seizures makes diagnosis and management particularly challenging.

Traditional methods of diagnosing epilepsy rely heavily on long-term electroencephalogram (EEG) recordings, which capture brainwave patterns over extended periods. However, visually analyzing these recordings is a time-consuming and labor-intensive process, often subject to human error. Neurologists must meticulously review hours of EEG data to identify subtle abnormalities indicative of seizure activity.

In recent years, the application of artificial intelligence (AI) to EEG analysis has emerged as a promising solution to overcome these limitations. AI algorithms, particularly those based on neural networks, can be trained to recognize complex patterns in EEG data with remarkable accuracy and speed. This opens the door to automated seizure detection systems that can assist neurologists in making faster and more reliable diagnoses.

The AI Revolution in EEG Analysis

AI analyzing EEG waveforms to detect seizures.

One particularly promising approach involves the use of Teager energy, a nonlinear measure that is sensitive to both the amplitude and frequency changes in EEG signals. Seizures are often characterized by rapid fluctuations in brainwave activity, making Teager energy an ideal feature for detecting these events. When combined with backpropagation neural networks, Teager energy analysis can achieve high levels of accuracy in distinguishing between normal brain activity and seizure patterns.

A recent study published in Australasian Physical & Engineering Sciences in Medicine explored the use of a multichannel EEG system combined with Teager energy analysis and a backpropagation neural network classifier. The researchers aimed to develop an automated system for detecting inter-ictal seizures, which occur between seizure events and can provide valuable diagnostic information. Data was collected from 14 patients with epilepsy, using multi-channel EEG recordings. Pre-processing steps included noise reduction using notch filters and independent component analysis to remove artifacts like eye blinks and muscle movements.

The study's methodology involved several key steps:

EEG data pre-processing to remove noise and artifacts.
Extraction of Teager energy features from the EEG signals.
Training a backpropagation neural network to classify EEG segments as either normal or epileptic.
Evaluating the performance of the classifier using metrics such as sensitivity, specificity, and false detection rate.

The results of the study were impressive, with the AI-powered system achieving a sensitivity of 96.66% and a specificity of 99.15%. This means that the system was able to correctly identify 96.66% of seizure events while also correctly identifying 99.15% of normal brain activity. The false detection rate was also low, at just 0.30 per hour. These findings suggest that the proposed AI-based system has the potential to be a valuable tool for assisting neurologists in the diagnosis of epilepsy.

The Future of Epilepsy Diagnosis

The application of AI to EEG analysis represents a significant step forward in the diagnosis and management of epilepsy. By automating the detection of seizure events and inter-ictal abnormalities, AI-powered systems can reduce the burden on neurologists, improve diagnostic accuracy, and enable earlier intervention for patients with epilepsy. As AI technology continues to evolve, we can expect even more sophisticated and effective tools to emerge, further transforming the landscape of epilepsy care. This could lead to more personalized treatment plans, better seizure control, and ultimately, improved quality of life for individuals living with epilepsy.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/s13246-018-0694-z, Alternate LINK

Title: Multichannel Eeg Based Inter-Ictal Seizures Detection Using Teager Energy With Backpropagation Neural Network Classifier

Subject: Radiology, Nuclear Medicine and imaging

Journal: Australasian Physical & Engineering Sciences in Medicine

Publisher: Springer Science and Business Media LLC

Authors: N. Sriraam, Kadeeja Tamanna, Leena Narayan, Mehraj Khanum, S. Raghu, A. S. Hegde, Anjani Bhushan Kumar

Published: 2018-10-18

Everything You Need To Know

How are epilepsy diagnoses traditionally made, and what are the challenges?

Epilepsy diagnosis traditionally relies on visual analysis of long-term electroencephalogram (EEG) recordings by neurologists. This involves meticulously reviewing hours of EEG data to identify subtle abnormalities indicative of seizure activity. The process is time-consuming, labor-intensive, and prone to human error due to the complexity and volume of data.

What is Teager energy, and why is it useful in AI-powered EEG analysis for detecting seizures?

Teager energy is a nonlinear measure used in AI-powered EEG analysis. It is particularly sensitive to amplitude and frequency changes in EEG signals, making it effective for detecting rapid fluctuations in brainwave activity characteristic of seizures. When combined with backpropagation neural networks, Teager energy analysis can accurately distinguish between normal brain activity and seizure patterns.

Can you elaborate on the methodology employed in the study involving the multi-channel EEG system?

The study utilized a multi-channel EEG system combined with Teager energy analysis and a backpropagation neural network classifier to detect inter-ictal seizures. EEG data was pre-processed using notch filters and independent component analysis to remove noise and artifacts. The backpropagation neural network was trained to classify EEG segments as either normal or epileptic, and its performance was evaluated using metrics like sensitivity, specificity, and false detection rate.

What were the key performance metrics of the AI-powered system in the study, and what do these metrics suggest about its potential?

The AI-powered system achieved a sensitivity of 96.66% and a specificity of 99.15%. This indicates that the system accurately identified 96.66% of seizure events and 99.15% of normal brain activity. The low false detection rate of 0.30 per hour further demonstrates the system's reliability in assisting neurologists with epilepsy diagnosis. While not explicitly mentioned in this study, it's crucial to note that external validation on diverse patient cohorts is essential to confirm the robustness and generalizability of these results.

What is the significance of AI in EEG analysis for epilepsy, and what future advancements can be expected?

AI's application to EEG analysis improves diagnostic accuracy and enables earlier intervention for epilepsy patients. AI-powered systems can automate the detection of seizure events and inter-ictal abnormalities, reducing the burden on neurologists. This leads to more personalized treatment plans, better seizure control, and an improved quality of life for individuals with epilepsy. The evolution of AI technology promises even more sophisticated and effective tools in epilepsy care, potentially leading to predictive models for seizure occurrence and personalized medication adjustments.