Ischemic stroke identification based on EEG and EOG using ID convolutional neural network and batch normalization

Giri, Endang Purnama; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni; Wijaya, Sastra Kusuma

doi:10.1109/icacsis.2016.7872780

Cited by 39 publications

(27 citation statements)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specifically, authors selected the delta (1-4 Hz), theta (5-8 Hz), alpha (9-13 Hz), lower beta (14-16 Hz), higher beta (17-30 Hz), and gamma (31-40 Hz) bands for mental workload state recognition. Moreover, other studies employed a combination of features, for instance [48], which used PSD features, as well as entropy, kurtosis, fractal component, among others, as input of the proposed CNN for ischemic stroke detection.…”

Section: Featuresmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Featuresmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…So far, the applications were mostly limited to specific diagnoses such as Alzheimer's disease [1], depression [2,3], traumatic brain injuries [4], or stroke [5]. They used a large variety of machine-learning techniques, including k-nearest neighbors, random forests, support vector machines, linear discriminant analysis, logistic…”

mentioning

confidence: 99%

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Schirrmeister

Gemein

Eggensperger

et al. 2017

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

143

View full text Add to dashboard Cite

Abstract-We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ≈85% vs. ≈79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.I. I Electroencephalography (EEG) is widely used in clinical practice because of its low cost and its lack of side effects due to its noninvasive nature. It is important both as a screening method as well as for hypothesis-based diagnostics, e.g., in epilepsy or stroke. One of the main limitations of using EEG for diagnostics is the required time and specialized knowledge of experts that need to be well-trained on EEG diagnostics to reach reliable results. Therefore, a machine-learning approach that aids in the diagnostic process could make EEG diagnosis more widely accessible, reduce time and effort for clinicians and potentially make diagnoses more accurate.In recent years researchers have increasingly addressed the field of computer-aided EEG diagnosis. So far, the applications were mostly limited to specific diagnoses such as Alzheimer's disease regression, neural networks, and more. This large variety of used methods indicates that the search for the best decoding approach for diverse types of EEG diagnosis is still ongoing.To overcome the lack of large datasets representative of the variety of EEG-diagnosable diseases and the heterogeneity of clinical populations, the Temple University Hospital (TUH) has published an unprecedented public dataset of clinical EEG recordings [6]. From this dataset with over 16000 clinical recordings, the TUH Abnormal EEG Corpus with about 3000 recordings has been created specifically to foster the development of methods for distinguishing pathological from normal EEG. Due to its size and rich annotatio...

show abstract

“…One common approach that previous studies have used for classifying EEG signals was feature extraction from the frequency and time-frequency domains utilizing the theory behind EEG band frequencies [8]: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20) and gamma (20-64 Hz). Truong et al [9] used Short-Time Fourier Transform (STFT) on a 30 second sliding window to train a three layer CNN on stacked time-frequency representations for seizure prediction and evaluated their method on three EEG databases.…”

Section: Introductionmentioning

confidence: 99%

Signal2Image Modules in Deep Neural Networks for EEG Classification

Bizopoulos¹,

Lambrou²,

Koutsouris³

2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks.In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as 'base models'. We compare the accuracy and time performance of four S2Is ('signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of 'base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and present visual comparisons of the outputs of some of the S2Is.

show abstract

Ischemic stroke identification based on EEG and EOG using ID convolutional neural network and batch normalization

Cited by 39 publications

References 6 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Signal2Image Modules in Deep Neural Networks for EEG Classification

Contact Info

Product

Resources

About