C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Recent developments in brain-machine interface technology have rendered seizure prediction possible. However, the transmission of a large volume of electrophysiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction...
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10012381/ |
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| author | Di Wu Yi Shi Ziyu Wang Jie Yang Mohamad Sawan |
| author_facet | Di Wu Yi Shi Ziyu Wang Jie Yang Mohamad Sawan |
| author_sort | Di Wu |
| collection | DOAJ |
| description | Recent developments in brain-machine interface technology have rendered seizure prediction possible. However, the transmission of a large volume of electrophysiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose <inline-formula> <tex-math notation="LaTeX">$\text{C}^{{2}}$ </tex-math></inline-formula>SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16. |
| format | Article |
| id | doaj-art-6afd900879d84256aa2aedc0a284c785 |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-6afd900879d84256aa2aedc0a284c7852025-08-20T03:07:14ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-013184185010.1109/TNSRE.2023.323539010012381C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure PredictionDi Wu0https://orcid.org/0000-0001-6589-7136Yi Shi1Ziyu Wang2Jie Yang3https://orcid.org/0000-0002-4148-0042Mohamad Sawan4https://orcid.org/0000-0002-4137-7272College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaCenter of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, ChinaCenter of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, ChinaCenter of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, ChinaCenter of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, ChinaRecent developments in brain-machine interface technology have rendered seizure prediction possible. However, the transmission of a large volume of electrophysiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose <inline-formula> <tex-math notation="LaTeX">$\text{C}^{{2}}$ </tex-math></inline-formula>SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.https://ieeexplore.ieee.org/document/10012381/Seizure predictionEEGconvolutional neural networkcompressive sensing (CS)hardware-friendly |
| spellingShingle | Di Wu Yi Shi Ziyu Wang Jie Yang Mohamad Sawan C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction IEEE Transactions on Neural Systems and Rehabilitation Engineering Seizure prediction EEG convolutional neural network compressive sensing (CS) hardware-friendly |
| title | C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction |
| title_full | C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction |
| title_fullStr | C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction |
| title_full_unstemmed | C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction |
| title_short | C<sup>2</sup>SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction |
| title_sort | c sup 2 sup sp net joint compression and classification network for epilepsy seizure prediction |
| topic | Seizure prediction EEG convolutional neural network compressive sensing (CS) hardware-friendly |
| url | https://ieeexplore.ieee.org/document/10012381/ |
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