Wearable seizure detection using convolutional neural networks with transfer learning

Page, Adam; Shea, Colin; Mohsenin, Tinoosh

doi:10.1109/iscas.2016.7527433

Cited by 54 publications

(28 citation statements)

References 10 publications

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“…A recent, prominent example of such an advance in machine learning is the application of convolutional neural networks (ConvNets), particularly in computer vision tasks. Thus, first studies have started to investigate the potential of ConvNets for brain‐signal decoding [Antoniades et al, ; Bashivan et al, ; Cecotti and Graser, ; Hajinoroozi et al, ; Lawhern et al, ; Liang et al, ; Manor et al, ; Manor and Geva, ; Page et al, ; Ren and Wu, ; Sakhavi et al, ; Shamwell et al, ; Stober, ; Stober et al, ; Sun et al, ; Tabar and Halici, ; Tang et al, ; Thodoroff et al, ; Wang et al, ] (see Supporting Information, Section A.1 for more details on these studies). Still, several important methodological questions on EEG analysis with ConvNets remain, as detailed below and addressed in this study.…”

Section: Introductionmentioning

confidence: 99%

Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister

Springenberg

Fiederer

et al. 2017

Human Brain Mapping

2,150

2,128

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Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister

Springenberg

Fiederer

et al. 2017

Human Brain Mapping

2,150

2,128

View full text Add to dashboard Cite

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“…Antoniades et al have considered generating feature automatically from epileptic intracranial EEG data in time domain by deep leaning. Page et al have made an end‐to‐end learning by max‐pooling convolutional neural networks (MPCNN) and demonstrated that transfer‐learning can be used to teach MPCNNs generalized features of raw EEG data. Acharya et al have presented a deep convolutional neural networks with 5 layers to detected normal, preictal, and seizure classes.…”

Section: Introduction and Methodologies For Eeg Data Analysismentioning

confidence: 99%

Deep learning for EEG data analytics: A survey

Lee

Jung

et al. 2019

Concurrency and Computation

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Summary In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

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“…This practice is in its infancy in the Omics and BBMI. We found few papers referring to the transfer learning (e.g., [258,259] for seizure detection, [234] for mental task classification and [116] for enhancers prediction). An original transfer learning was done by Tan et al [275], who transferred knowledge from ImageNet computer vision database to an EEG signal classification.…”

Section: Biomedical Data and Transfer Learningmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

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Wearable seizure detection using convolutional neural networks with transfer learning

Cited by 54 publications

References 10 publications

Deep learning with convolutional neural networks for EEG decoding and visualization

Deep learning with convolutional neural networks for EEG decoding and visualization

Deep learning for EEG data analytics: A survey

Deep Learning in the Biomedical Applications: Recent and Future Status

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