A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform

Yuan, Ye; Xun, Guangxu; Jia, Kebin; Zhang, Aidong

doi:10.1145/3107411.3107419

Cited by 84 publications

(47 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For medical concept embedding (eg., embed different medical codes in a common space), AEs are a preferred family of models. 10 , 30 , 63 , 78 , 79 , 82–84 An AE [see Figure 2(e) ] maps inputs to an internal code representation through an encoder, and then maps the low-dimensional representation back to the input space through a decoder. The composition of encoder and decoder is called the reconstruction function.…”

Section: Resultsmentioning

confidence: 99%

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Xiao¹,

Choi

Sun

2018

Journal of the American Medical Informatics Association

578

346

View full text Add to dashboard Cite

ObjectiveTo conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs.Design/methodWe searched PubMed and Google Scholar for papers on deep learning studies using EHR data published between January 1, 2010, and January 31, 2018. We summarize them according to these axes: types of analytics tasks, types of deep learning model architectures, special challenges arising from health data and tasks and their potential solutions, as well as evaluation strategies.ResultsWe surveyed and analyzed multiple aspects of the 98 articles we found and identified the following analytics tasks: disease detection/classification, sequential prediction of clinical events, concept embedding, data augmentation, and EHR data privacy. We then studied how deep architectures were applied to these tasks. We also discussed some special challenges arising from modeling EHR data and reviewed a few popular approaches. Finally, we summarized how performance evaluations were conducted for each task.DiscussionDespite the early success in using deep learning for health analytics applications, there still exist a number of issues to be addressed. We discuss them in detail including data and label availability, the interpretability and transparency of the model, and ease of deployment.

show abstract

Section: Resultsmentioning

confidence: 99%

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Xiao¹,

Choi

Sun

2018

Journal of the American Medical Informatics Association

578

346

View full text Add to dashboard Cite

show abstract

“…Firstly, we describe the literature that regards the transformed signals as raw features. By using a deep learning approach, the STFT spectrogram of a raw EEG signal was considered as a feature with a modified stacked sparse denoising autoencoder (mSSDA) as a classifier [YXJZ17]. This combination was then compared to the other classifiers, where the result showed that the mSSDA could successfully distinguish epilepsy from normality.…”

Section: Time-frequency-domain Features (Tfdfs)mentioning

confidence: 99%

A review of feature extraction and performance evaluation in epileptic seizure detection using EEG

Boonyakitanont

Lek-uthai

Chomtho

et al. 2020

Biomedical Signal Processing and Control

250

111

View full text Add to dashboard Cite

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features -variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients-were able to significantly capture the seizures. An improvement of 4.77-13.51% in the Bayesian error from the baseline was obtained.

show abstract

“…Then, the data was fed into a recurrent convolutional neural network that combines CNN and LSTM networks. The method presented by Yuan et al [23] detects epileptic seizure onsets. It combines features extracted from handcrafted engineering methods and deep learning approaches.…”

Section: Epileptic Seizure Detectionmentioning

confidence: 99%

“…Existing approaches [3,8,[10][11][12]17,[19][20][21]23] can be divided into two categories. The first category [3,6,8,16,19,20,23] includes seizure detection approaches that are usually used by health professionals to improve diagnostic capabilities. In fact, they do not allow us to prevent the consequences of epileptic seizures.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction

Affes

Mdhaffar

Triki

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, we present a convolutional gated recurrent neural network (CGRNN) to predict epileptic seizures based on features extracted from EEG data that represent the temporal aspect and the frequency aspect of the signal. Using a dataset collected in the Children's Hospital of Boston, CGRNN can predict epileptic seizures between 35 min and 5 min in advance. Our experimental results indicate that the performance of CGRNN varies between patients. We achieve an average sensitivity of 89% and a mean accuracy of 75.6% for the patients in the data set, with a mean False Positive Rate (FPR) of 1.6 per hour.

show abstract

A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform

Cited by 84 publications

References 30 publications

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

A review of feature extraction and performance evaluation in epileptic seizure detection using EEG

A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction

Contact Info

Product

Resources

About