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

Tsiouris, Κostas Μ.; Pezoulas, Vasileios C.; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios; Fotiadis, Dimitrios I.

doi:10.1016/j.compbiomed.2018.05.019

Cited by 438 publications

(216 citation statements)

References 42 publications

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“…Similarly, in [26], balanced accuracy was maximized by using a balanced sampling strategy. In [183], EEG segments from the interictal class were split into smaller subgroups of equal size to the preictal class. In [160], cost-sensitive learning and oversampling were used to solve the class imbalance problem for sleep staging but the overall performance using these approaches did not improve.…”

Section: Data Augmentationmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Data Augmentationmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…Addressing both these requirements formed the intuition behind our proposed solution of using an LSTM network with the attention mechanism. LSTM has been widely utilized for learning and classifying time-series data including bio-signals [22], [25]. Moreover, recent studies have successfully used LSTM architectures for EEG analysis given the time-dependant nature of these signals [21].…”

Section: Proposed Deep Learning Solutionmentioning

confidence: 99%

Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

et al. 2020

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Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical applications. This paper proposes a novel solution for classification of left/right hand movement by exploiting a Long Short-Term Memory (LSTM) network with attention mechanism to learn the electroencephalogram (EEG) time-series information. To this end, a wide range of time and frequency domain features are extracted from the EEG signals and used to train an LSTM network to perform the classification task. We conduct extensive experiments with the EEG Movement dataset and show that our proposed solution our method achieves improvements over several benchmarks and state-of-the-art methods in both intrasubject and cross-subject validation schemes. Moreover, we utilize the proposed framework to analyze the information as received by the sensors and monitor the activated regions of the brain by tracking EEG topography throughout the experiments.

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“…Although deep learning models extract features by themselves and can be applied directly to the raw data [Sors et al, 2018;Tang et al, 2017], one can still use preprocessed signal as model input [Johansen et al, 2016;Tsiouris et al, 2018]. Deep learning models also often used as feature extractors [Ansari et al, 2018;Wang et al, 2017].…”

Section: Artificial Intelligencementioning

confidence: 99%