Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

By N. D. Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Samuel J. Ippolito, O. Kavehei. Neural Networks, 2018

565 citations recorded by the source corpus.

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/88cad20ad267902b1241cb3e8a713529d3d52538

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In Cognitive Neuroscience

Citations in the corpus

EEG based multi-class seizure type classification using convolutional neural network and transfer learning (2020, methodology): “…electroencephalogram signals achieved a sensitivity of 81.4%, 81.2%, and 75.0% using the Freiburg Hospital intracranial EEG dataset, the Boston Children’s Hospital-MIT scalp EEG dataset, and the American Epilepsy Society seizure prediction challenge dataset respectively (Truong, et al., 2018).”
Deep learning-based electroencephalography analysis: a systematic review (2019, methodology): “In [200], an overlapping window with a subject-specific overlap was used to match classes.”
Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals (2022, methodology): “put the time–frequency information obtained by the short-time Fourier transform (STFT) into CNN to classify the nonseizure and seizure periods [25].”