Unsupervised feature learning based on autoencoder for epileptic seizures prediction

He, Peng; Wang, Linhai; Cui, Yaping; Wang, Ruyan; Wang, Dapeng

doi:10.1007/s10489-023-04582-9

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Insufficient data: DL algorithms work best when there are large numbers of samples available for training which is often not the case. Data augmentation [84] and transfer learning [85] are technical strategies that can be adopted to overcome insufficient datasets. Data augmentation involves the generation of new or slightly modified samples to increase the sample numbers for training the algorithms, which, in turn, improves the performance of the models [84].…”

Section: Challenges and Potential Solutionsmentioning

confidence: 99%

Applications for Deep Learning in Epilepsy Genetic Research

Zeibich,

Kwan,

J. O’Brien

et al. 2023

IJMS

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Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.

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Section: Challenges and Potential Solutionsmentioning

confidence: 99%

Applications for Deep Learning in Epilepsy Genetic Research

Zeibich,

Kwan,

J. O’Brien

et al. 2023

IJMS

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“…It can be applied during the recording of EEG signals, eliminating the need for data annotation and individual feature extraction methods for each patient. This technique has mainly been used in seizure detection and has achieved high sensitivity and specificity [22,24,25]. Currently, there are two main techniques utilized: autoencoders (AE) and deep convolutional generative adversarial networks (DCGAN) [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Seizure Prediction Model Combining Generative Adversarial Networks and Long Short-Term Memory Networks

Yang,

Liu,

et al. 2023

Applied Sciences

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In recent years, significant progress has been made in seizure prediction using machine learning methods. However, fully supervised learning methods often rely on a large amount of labeled data, which can be costly and time-consuming. Unsupervised learning overcomes these drawbacks but can suffer from issues such as unstable training and reduced prediction accuracy. In this paper, we propose a semi-supervised seizure prediction model called WGAN-GP-Bi-LSTM. Specifically, we utilize the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as the feature learning model, using the Earth Mover’s distance and gradient penalty to guide the unsupervised training process and train a high-order feature extractor. Meanwhile, we built a prediction model based on the Bidirectional Long Short-Term Memory Network (Bi-LSTM), which enhances seizure prediction performance by incorporating the high-order time-frequency features of the brain signals. An independent, publicly available dataset, CHB-MIT, was applied to train and validate the model’s performance. The results showed that the model achieved an average AUC of 90.08%, an average sensitivity of 82.84%, and an average specificity of 85.97%. A comparison with previous research demonstrates that our proposed method outperforms traditional adversarial network models and optimizes unsupervised feature extraction for seizure prediction.

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