A Low Complexity Estimation Method of Entropy for Real-Time Seizure Detection

Shyu, Kuo-Kai; Huang, Szu‐chi; Lee, Lung-Hao; Lee, Po-Lei

doi:10.1109/access.2023.3235913

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…Tang et al [22] (2023) ESN+FDEF c Bonn/CHB -MIT N.A 98.1/96.14 Shyu et al [28] Among the comparison methods discussed above, the approaches proposed in [38] and [21] both achieve an accuracy of approximately 97%, but their network parameters are extensive, hundreds or even over a thousand times that of the methods proposed in this study. This makes them more challenging to train and places higher demands on hardware equipment.…”

Section: ) Feature Extraction and Classificationmentioning

confidence: 88%

“…This makes them more challenging to train and places higher demands on hardware equipment. References [22] and [39] employ traditional machine learning methods, and [20] uses a combination of deep learning and machine learning methods. Both of these methods also yield good recognition results, but their principles are complex and require manual extraction of the right features.…”

Section: Discussionmentioning

confidence: 99%

“…Shyu et al [39] (2023) LBPMAD b Bonn N.A 97. 13 Shen et al [20] (2023) Q-wavelet tansform+CNN CHB-MIT N.A 97.57…”

Section: ) Feature Extraction and Classificationmentioning

confidence: 99%

“…Qiu et al [26] proposed a difference attention ResNet-LSTM network (DARLNet), achieving 98.17% accuracy. Zhao et al [27] used a random channel ordering (OCR) method for data augmentation, and Shyu et al [28] proposed a model with a combination of inception module and residual module, achieving an accuracy of 97.11%.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network

Wang,

Liu,

Zhuo

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Objective: Epilepsy, an enduring neurological disorder, afflicts approximately 65 million individuals globally, significantly impacting their physical and mental wellbeing. Traditional epilepsy detection methods are labor-intensive, leading to inefficiencies. Although deep learning techniques for brain signal detection have gained traction in recent years, their clinical application advancement is hindered by the significant requirement for high-quality data and computational resources during training. Methods & Results: The neural network training initially involved merging two datasets of different data quality, namely Bonn University datasets and CHB-MIT datasets, to bolster its generalization capabilities. To tackle the issues of dataset size and class imbalance, we employed small window segmentation and Synthetic Minority Over-sampling Technique (SMOTE). algorithms to augment and equalize the data. A streamlined neural network architecture was then proposed, drastically reducing the model's training parameters. Notably, a model trained with a mere 9,371 parameters yielded impressive results. The three-classification task on the combined dataset delivered an accuracy of 98.52%, sensitivity of 97.99%, specificity of 99.35%, and precision of 98.44%.Conclusions: The experimental findings of this study underscore the superiority of the proposed method over existing approaches in both model size reduction and accuracy enhancement. As a result, it is more apt for deployment in low-cost, low computational hardware devices, including wearable technology, and various clinical applications.

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Section: ) Feature Extraction and Classificationmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

“…Shyu et al [39] (2023) LBPMAD b Bonn N.A 97. 13 Shen et al [20] (2023) Q-wavelet tansform+CNN CHB-MIT N.A 97.57…”

Section: ) Feature Extraction and Classificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network

Wang,

Liu,

Zhuo

et al. 2024

IEEE J. Transl. Eng. Health Med.

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“…However, for EEG signals with high noise levels, this method may face limitations. Shyu et al [15] achieved notable EEG epilepsy detection results with their parameter-optimized Inception-based end-to-end CNN model, but such end-to-end models might encounter flexibility and adjustability challenges in practical applications [16].…”

Section: Introductionmentioning

confidence: 99%

Technological Vanguard: The Outstanding Performance of the LTY-CNN Model for the Early Prediction of Epileptic Seizures

yang,

Luan,

et al. 2023

Preprint

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Background: Epilepsy is a common neurological disorder that affects approximately 60 million people worldwide. Characterized by unpredictable neural electrical activity abnormalities, it results in seizures with varying intensity levels. Electroencephalography (EEG), as a crucial technology for monitoring and predicting epileptic seizures, plays an essential role in improving the quality of life for people with epilepsy. Method: This study introduces an innovative deep learning model, a lightweight triscale yielding convolutional neural network” (LTY-CNN), that is specifically designed for EEG signal analysis. The model integrates a parallel convolutional structure with a multihead attention mechanism to capture complex EEG signal features across multiple scales and enhance the efficiency achieved when processing time series data. The lightweight design of the LTY-CNN enables it to maintain high performance in environments with limited computational resources while preserving the interpretability and maintainability of the model. Results: In tests conducted on the SWEC-ETHZ and CHB-MIT datasets, the LTY-CNN demonstrated outstanding performance. On the SWEC-ETHZ dataset, the LTY-CNN achieved an accuracy of 99.9%, an area under the receiver operating characteristic curve (AUROC) of 0.99, a sensitivity of 99.9%, and a specificity of 99.8%. Furthermore, on the CHB-MIT dataset, it recorded an accuracy of 99%, an AUROC of 0.932, a sensitivity of 99.1%, and a specificity of 93.2%. These results signify the remarkable ability of the LTY-CNN to distinguish between epileptic seizures and nonseizure events. Compared to other existing epilepsy detection classifiers, the LTY-CNN attained higher accuracy and sensitivity. Conclusion: The high accuracy and sensitivity of the LTY-CNN model demonstrate its significant potential for epilepsy management, particularly in terms of predicting and mitigating epileptic seizures. Its value in personalized treatments and widespread clinical applications reflects the broad prospects of deep learning in the health care sector. This also highlights the crucial role of technological innovation in enhancing the quality of life experienced by patients.

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RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals

Zhou,

Zhang,

et al. 2024

Computers in Biology and Medicine

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A Low Complexity Estimation Method of Entropy for Real-Time Seizure Detection

Cited by 10 publications

References 33 publications

An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network

An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network

Technological Vanguard: The Outstanding Performance of the LTY-CNN Model for the Early Prediction of Epileptic Seizures

RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals

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