A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning

Yuan, Ye; Xun, Guangxu; Ma, Fenglong; Suo, Qiuling; Xue, Hongfei; Jia, Kebin; Zhang, Aidong

doi:10.1109/bhi.2018.8333405

Cited by 53 publications

(31 citation statements)

References 11 publications

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“…For deeper layers, however, the hierarchical nature of neural networks means it is much harder to understand what a weight is applied to. The analysis of model activations was used in multiple studies [212,194,87,83,208,167,154,109]. This kind of inspection method usually involves visualizing the activations of the trained model over multiple examples, and thus inferring how different parts of the network react to known inputs.…”

Section: Inspection Of Trained Modelsmentioning

confidence: 99%

“…Occlusion sensitivity techniques [92,26,175] use a similar idea, by which the decisions of the network when different parts of the input are occluded are analyzed. [135,211,86,34,87,200,182,122,170,228,164,109,204,85,25] Analysis of activations [212,194,87,83,208,167,154,109] Input-perturbation network-prediction correlation maps [149,191,67,16,150] Generating input to maximize activation [188,144,160,15] Occlusion of input [92,26,175] Several studies used backpropagation-based techniques to generate input maps that maximize activations of specific units [188,144,160,15]. These maps can then be used to infer the role of specific neurons, or the kind of input they are sensitive to.…”

Section: Inspection Of Trained Modelsmentioning

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: Inspection Of Trained Modelsmentioning

confidence: 99%

Section: Inspection Of Trained Modelsmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…In our experiment, each EEG fragment is labeled based on the ground truth as in one of the two classes: ictal and non-ictal states. Taking the computational expense into consideration, we adopt hold-out validation in the same way to [ 17 , 32 , 33 ]. Note that the holding-out portions of the dataset is a manner similar to cross-validation.…”

Section: Resultsmentioning

confidence: 99%

A multi-context learning approach for EEG epileptic seizure detection

et al. 2018

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BackgroundEpilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroencephalogram (EEG) is the most commonly used biological measurement to effectively capture the abnormalities of different brain areas during the EEG seizures. To avoid manual visual inspection from long-term EEG readings, automatic epileptic EEG seizure detection has become an important research issue in bioinformatics.ResultsWe present a multi-context learning approach to automatically detect EEG seizures by incorporating a feature fusion strategy. We generate EEG scalogram sequences from the EEG records by utilizing waveform transform to describe the frequency content over time. We propose a multi-stage unsupervised model that integrates the features extracted from the global handcrafted engineering, channel-wise deep learning, and EEG embeddings, respectively. The learned multi-context features are subsequently merged to train a seizure detector.ConclusionsTo validate the effectiveness of the proposed approach, extensive experiments against several baseline methods are carried out on two benchmark biological datasets. The experimental results demonstrate that the representative context features from multiple perspectives can be learned by the proposed model, and further improve the performance for the task of EEG seizure detection.

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“…ChannelAtt [18]. ChannelAtt adopts fully connected multi-view learning to soft-select critical channels from multi-channel biosignals.…”

Section: Methodsmentioning

confidence: 99%

“…Choi et al [16] and Ma et al [17] adopted attention mechanisms to explain medical codes (e.g., procedure, diagnosis, and medication codes) from electronic health records (EHRs). Yuan et al [18] first exploited an attention mechanism based on multi-view learning (i.e., ChannelAtt) to achieve soft channel selection for EEG seizure detection. In general, there are two differences between ChannelAtt and our proposed FusionAtt model.…”

Section: Related Workmentioning

confidence: 99%

FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals

Yuan

Jia

2019

Sensors

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Recently, pervasive sensing technologies have been widely applied to comprehensive patient monitoring in order to improve clinical treatment. Various types of biomedical signals collected by different sensing channels provide different aspects of patient health information. However, due to the uncertainty and variability in clinical observation, not all the channels are relevant and important to the target task. Thus, in order to extract informative representations from multi-channel biosignals, channel awareness has become a key enabler for deep learning in biosignal processing and has attracted increasing research interest in health informatics. Towards this end, we propose FusionAtt—a deep fusional attention network that can learn channel-aware representations of multi-channel biosignals, while preserving complex correlations among all the channels. FusionAtt is able to dynamically quantify the importance of each biomedical channel, and relies on more informative ones to enhance feature representation in an end-to-end manner. We empirically evaluated FusionAtt in two clinical tasks: multi-channel seizure detection and multivariate sleep stage classification. Experimental results showed that FusionAtt consistently outperformed the state-of-the-art models in four different evaluation measurements, demonstrating the effectiveness of the proposed fusional attention mechanism.

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A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning

Cited by 53 publications

References 11 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

A multi-context learning approach for EEG epileptic seizure detection

FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals

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