Gated recurrent networks for seizure detection

Golmohammadi, Meysam; Ziyabari, Saeedeh; Shah, Vinit; Weltin, Eva von; Campbell, C. M.; Obeid, Iyad; Picone, J.

doi:10.1109/spmb.2017.8257020

Cited by 58 publications

(49 citation statements)

References 24 publications

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“…By updating the data items table regularly and inviting researchers in the community to contribute, we hope to keep the supplementary material of the review relevant and up-to-date as long as possible. Provide a table or figure clearly describing your model (e.g., see [26,51,150]). 2 Clearly describe the data used.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…As a result, various factors that impact the performance of DL-EEG were not covered in the review. For example, we did not cover weight initialization: in [51], the authors compared 10 different initialization methods and showed an impact on the specificity metric, with ranged from 85.1% to 96.9%. Similarly, multiple data items were collected during the review process, but were not included in the analysis.…”

Section: Limitationsmentioning

confidence: 99%

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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: Supplementary Materialsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…The depth of the convolutional network is important since the top convolutional layers tend to learn generic features while the deeper layers learn dataset specific features. The convolutional LSTM architecture with proper initialization and regularization delivers 30% sensitivity at 6 false alarms per 24 hours [10]. Feature extraction typically relies on time frequency representations of the signal.…”

Section: Introductionmentioning

confidence: 99%

Optimizing channel selection for seizure detection

Shah

Golmohammadi

Ziyabari

et al. 2017

2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when fewer electrodes are used, less spatial information is available, making it harder to detect artifacts. In this study, we investigate the performance of a deep learning algorithm, CNN-LSTM, on several channel configurations. Each configuration was designed to minimize the amount of spatial information lost compared to a standard 22-channel EEG. Systems using a reduced number of channels ranging from 8 to 20 achieved sensitivities between 33% and 37% with false alarms in the range of [38, 50] per 24 hours. False alarms increased dramatically (e.g., over 300 per 24 hours) when the number of channels was further reduced. Baseline performance of a system that used all 22 channels was 39% sensitivity with 23 false alarms. Since the 22-channel system was the only system that included referential channels, the rapid increase in the false alarm rate as the number of channels was reduced underscores the importance of retaining referential channels for artifact reduction. This cautionary result is important because one of the biggest differences between various types of EEGs administered is the type of referential channel used.

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“…In addition, over the past years standard RNNs and LSTMs have gone through several structural modifications which led to the rise of a simplified version of LSTM, called GRU. GRUs are widely applied and have shown their effectiveness for speech processing, medical image processing and video segmentation tasks [14,30,32]. Despite their consumption for different computer vision problems, abnormal event detection has not taken full advantage of GRUs' inherent nature to develop optimized and effective models, and much effort has not been put forth to exploit GRUs for this task.…”

Section: Introductionmentioning

confidence: 99%

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

Fanta

Shao

2020

Information Sciences

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A B S T R A C TAbnormality detection is a challenging task due to the dependence on a specific context and the unconstrained variability of practical scenarios. In recent years, it has benefited from the powerful features learnt by deep neural networks, and handcrafted features specialized for abnormality detectors. However, these approaches with large complexity still have limitations in handling long-term sequential data (e.g., videos), and their learnt features do not thoroughly capture useful information. Recurrent Neural Networks (RNNs) have been shown to be capable of robustly dealing with temporal data in long-term sequences. In this paper, we propose a novel version of Gated Recurrent Unit (GRU), called Single-Tunnelled GRU for abnormality detection. Particularly, the Single-Tunnelled GRU discards the heavy-weighted reset gate from GRU cells that overlooks the importance of past content by only favouring current input to obtain an optimized single-gated-cell model. Moreover, we substitute the hyperbolic tangent activation in standard GRUs with sigmoid activation, as the former suffers from performance loss in deeper networks. Empirical results show that our proposed optimized-GRU model outperforms standard GRU and Long Short-Term Memory (LSTM) networks on most metrics for detection and generalization tasks on CUHK Avenue and UCSD datasets. The model is also computationally efficient with reduced training and testing time over standard RNNs.

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Gated recurrent networks for seizure detection

Cited by 58 publications

References 24 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

Optimizing channel selection for seizure detection

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

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