Machine Learning Applications in Epilepsy

By B. Abbasi, Daniel M. Goldenholz. Epilepsia, 2019

304 citations recorded by the source corpus.

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/be1496e9620089b377ef631692478f5034ee95b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Rubén Morales-Menéndez
2. Xiaodi Huang
3. Mohammad Khubeb Siddiqui
4. Nasir Hussain

In Computer Vision and Pattern Recognition

1. Rubén Morales-Menéndez
2. Xiaodi Huang
3. Mohammad Khubeb Siddiqui
4. Nasir Hussain

In Psychiatry and Mental health

1. Rubén Morales-Menéndez
2. Xiaodi Huang
3. Mohammad Khubeb Siddiqui
4. Nasir Hussain

Citations in the corpus

• A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “This has been done by various machine learning classifiers such as ANN, SVM, decision tree, decision forest, and random forest [26, 28].”