A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

By Kostas M. Tsiouris, Vasileios C. Pezoulas, M. Zervakis, Spiros Konitsiotis, Dimitrios D. Koutsouris, Dimitrios I. Fotiadis. Comput. Biol. Medicine, 2018

546 citations recorded by the source corpus.

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/9e56cf7855a0206fd2b2554bc5d9b36f85467794

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. Chin-Teng Lin
2. Peng Xu
3. Alireza Jolfaei
4. Xiaotong Gu
5. Zehong Cao
6. Tzyy-Ping Jung
7. Dongrui Wu
8. Adeel Razi

In Psychiatry and Mental health

1. Adeel Razi
2. Patrick Kwan
3. Adnan Qayyum

Citations in the corpus

• Deep learning-based electroencephalography analysis: a systematic review (2019, background): “[2, 45, 145, 202]), while others employed grid search (e.”
• Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, methodology): “[73] used long short-term memory (LSTM) for the first time for the prediction of an epileptic seizure.”
• EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “[173] introduced a two-layer LSTM network to assess seizure prediction performance by exploiting a broad scope of features before classification between EEG channels.”
• Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “Similar to [89], a 2-layer LSTM is used with a bi-”