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  • Review Article
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Artificial intelligence in epilepsy — applications and pathways to the clinic

Nature Reviews Neurology (2024)Cite this article

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Abstract

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

Key points

  • Artificial intelligence (AI) has the potential to maximize the value of data collected throughout the management of epilepsy, including neuroimaging and electroencephalography data, electronic medical records, and data from medical devices.

  • Machine learning dominated early applications of AI in epilepsy, but deep learning approaches have become increasingly popular.

  • Despite development of many AI tools with potential for the diagnosis and management of epilepsy, few have been implemented in clinical practice.

  • Collaborative efforts, including sharing of data and expertise, among researchers and clinicians are essential to realize the full potential of AI in epilepsy management.

  • Methodological and ethical considerations are pivotal for integrating AI into routine epilepsy management.

  • Future advancements in AI require robust clinical trials and ethical frameworks to ensure efficacy and patient safety in epilepsy treatment.

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Fig. 1: The main data modalities used with artificial intelligence in epilepsy.
Fig. 2: An overview of terms in artificial intelligence.
Fig. 3: Applications of artificial intelligence for interpretation of intracranial electroencephalography data.
Fig. 4: The future of artificial intelligence in epilepsy management.

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Acknowledgements

The authors acknowledge K. Xie (University of Pennsylvania) for his help with the section of the Review on electronic health records, and B. Scheid (University of Pennsylvania) and W. Ojemann (University of Pennsylvania) for their help with the section of the Review on medical devices. All provided useful references that enriched the content.

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Author notes

  1. These authors contributed equally: Alfredo Lucas, Andrew Revell.

Authors and Affiliations

  1. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Alfredo Lucas, Andrew Revell & Kathryn A. Davis

  2. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Alfredo Lucas

  3. Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA

    Kathryn A. Davis

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Glossary

Approximate zero-crossing

Point in a waveform where the signal crosses the zero line, useful in signal processing and analysis.

Area under the receiver operating curve

A measure of how well a model can distinguish between different outcomes; higher values indicate better performance.

Autoencoder

A neural network designed to learn embeddings of the input data through an encoding–decoding scheme.

Bandpower

A measure of the power of a signal’s frequency components, often restricted to a specific range of frequencies.

Betweenness centrality

A measure of how often a node appears on the shortest paths between other nodes in a network, indicating its role as a bridge or connector.

Bidirectional encoder representations from transformers

A technique in natural language processing that helps computers to understand the context of words in sentences more effectively.

Class imbalance

Occurs in data when one group of data (a class) is much larger than others, making it harder for machine learning models to generalize to datasets with a different distribution of classes.

Convolutional neural network

A neural network that is especially good for analysing visual data, such as images, by recognizing patterns and structures within them.

Cortical outlier detection

A technique that identifies unusual structures or patterns in brain imaging data; useful for studying brain health and disorders.

Decision trees

Machine learning models that predict the value of a target variable by learning simple decision rules inferred from the data features.

Deep learning

A type of machine learning that involves neural networks with many layers; excels in tasks such as image and speech recognition.

Deep residual neural network

A type of advanced neural network that is especially good at learning from very large amounts of data, with a unique feature of learning from its own errors to improve accuracy.

Degree centrality

A measure of how many connections or relationships a node has with others, indicating its importance or influence in the network.

Elastic net regression

A statistical method that combines two techniques (least absolute shrinkage and selection operator regression and ridge regression) to predict or estimate outcomes; useful for overcoming difficulties associated with datasets with a large number of features and overfitting to limited data.

Embedding

The process of mapping data onto a new space of higher or lower dimensionality, in which the data points are more clearly distinguishable or more easily manipulated.

Extreme gradient boosting algorithm

A powerful and efficient algorithm used for structured data prediction, known for its speed and performance.

False alarm rate

The rate at which a system incorrectly identifies a non-event as an event.

Fast Fourier transformation

A method for analysing the frequency components of signals (such as audio) over time, represented visually in a spectrogram.

Feature crafting

The process of selecting, manipulating and transforming raw data into features that better represent the underlying problem to predictive models.

Features

Distinct, measurable attributes, properties or characteristics within a dataset that act as inputs for artificial intelligence models.

Fractional anisotropy

A brain imaging measure that indicates how uniformly water flows in one direction, often used to assess the integrity of brain fibres.

F-score

A statistical measure of the accuracy of a model, especially when there is an uneven class distribution; encompasses the precision (the proportion of positive identifications that were correct) and the recall (the proportion of positives that were identified correctly) of the model.

Generative adversarial network

A framework for simultaneously training a generative model, which generates new data instances, and a discriminative model, which evaluates these data instances; the generative model learns to trick the discriminative model, creating data that looks like the inputs provided to the model.

Graph convolutional neural networks

Neural networks that operate directly on data that are structured as a connected graph (nodes with connections between them), enabling the model to take advantage of the graphical structure of data.

Graph theory metrics

Measures that derive from graph theory and are used to analyse the structures and connections within a set of data, such as social networks or brain connectivity networks.

Ground truth labels

The true labels or classifications of data used to train and evaluate machine learning models.

Held out dataset

A portion of data that is set aside and not used during training of a model and is later used to test and evaluate the performance of the model.

High-dimensional

Including a large number of attributes or variables, often making analysis more complex.

Hubness

The tendency of a node in a network to form a large number of connections with other nodes; hub nodes are central or highly influential within the network and strongly affect its structure and dynamics.

Independent component analysis

A method to separate a mixed set of signals into individual, uncorrelated components, often used in audio or image processing.

Integrated gradients

A method that helps to explain the decision-making of models, particularly in deep learning, by attributing the prediction to its input features.

K-means clustering

A method for grouping data into a specified number of clusters, where each data point belongs to the cluster with the nearest mean value.

Lagged global brain signal

A measure of time-delayed overall brain activity used in brain imaging analysis to understand brain network dynamics.

Large language models

Artificial intelligence models that are designed to understand, generate and interpret large amounts of natural language text.

Latent Dirichlet allocation

A generative statistical model traditionally used for topic modelling of documents; assumes each document includes a mixture of topics and iteratively refines its guesses about these topics and their associated words, revealing the main subjects discussed across a large set of documents.

Leave-one-site-out cross-validation

A method used to test the effectiveness of a model, particularly when data originate from different locations, by systematically leaving out data from one site at a time.

Linear discriminant analysis classifiers

Supervised learning methods that separate different groups or categories by identifying the features that most clearly distinguish them.

Logistic regression classifiers

Methods that calculate the odds of something happening (for example, an event or a choice) on the basis of specific influencing factors.

Long short-term memory recursive neural networks

A type of neural network that is especially good at processing sequences of data (such as text or time series) by remembering information for long periods.

Machine learning

A field of artificial intelligence in which algorithms learn from data and make predictions or decisions.

Mean diffusivity

A brain imaging measure that reflects how much water spreads out in tissue, helping to understand brain health and disease states.

Mean kurtosis

A brain imaging measure of the degree of variability in water diffusion, providing insight into the complexity of tissue structure.

Natural language processing

A branch of artificial intelligence that deals with training models to understand human natural language.

Neural network

A computing system inspired by the structure, processing methods and learning ability of the human brain, consisting of layers of interconnected ‘neurons’, each of which can process information and transmit signals to others in the network, and often employing non-linearities in their structure that enable recognition of patterns in complex data that the network learns from.

Non-negative matrix factorization

A technique used to break down a dataset into simpler parts, with the condition that none of these parts can have negative values; commonly used for data compression and pattern recognition.

Normative modelling

A modelling approach that involves creating a standard or ‘normal’ model based on data from a healthy population, which can then be used to spot irregularities or deviations in individual cases.

Quadratic discriminant analysis classifier

A technique that separates different groups or categories, similar to linear discriminant analysis classifiers but better suited to situations in which each group has unique characteristics.

Random forest classifier

A machine learning model that consists of many individual decision trees that operate as an ensemble, in which each tree makes a class prediction and the class that gets the most votes becomes the model’s prediction.

Recursive feature elimination

A technique for selecting important features in a dataset by repeatedly building a model and removing the least informative feature, as defined by the performance of the model trained with the set of features, each time.

Recursive neural networks

A type of neural network architecture designed for processing structured data, such as data with a temporal component (time-series data), or data that follows a specific pattern (language).

Resection mask

A mapped area, often in medical imaging, that shows the part of an organ or tissue planned for surgical removal.

Self-supervised learning

A type of machine learning in which the system teaches itself using part of the data as a guide, often used when labelled data are scarce.

Semi-supervised learning

A type of machine learning in which a small set of labelled data are combined with a large pool of unlabelled data to improve training efficiency and model performance.

Spectral clustering

A technique in which similar data points are grouped together on the basis of their relationships to each other, often used for complex clustering tasks.

Spiking neural networks

Neural networks that mimic the behaviour of biological neural networks, in which neurons fire in a discrete manner.

Supervised learning

A type of machine learning in which known examples are used to train the model such that it can then make predictions about new data.

Support vector classifier

A type of model used for classification tasks, which works by finding the best boundary to separate different categories of data.

Support vector regressor

A type of model used for predicting continuous outcomes, based on the principles of finding the best line (or plane in higher dimensions) to fit the data.

Topic modelling

The process of identifying topics present in a collection of documents.

Transfer learning

A method to expedite learning in a new task by transferring knowledge from a related task that has already been learned; for example, a model that has been trained for seizure detection on the basis of scalp electroencephalography can be re-trained for seizure detection on the basis of intracranial electroencephalography through transfer learning with fewer data than might otherwise be required.

Unsupervised learning

A type of machine learning in which hidden patterns in data are identified without known examples or labels guiding the model.

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Lucas, A., Revell, A. & Davis, K.A. Artificial intelligence in epilepsy — applications and pathways to the clinic. Nat Rev Neurol (2024). https://doi.org/10.1038/s41582-024-00965-9

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