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SciCNN: A 0-Shot-Retraining Patient-Independent Epilepsy-Tracking SoC
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tsai, Chne-Wuen | - |
dc.contributor.author | Jiang, Rucheng | - |
dc.contributor.author | Zhang, Lian | - |
dc.contributor.author | Zhang, Miaolin | - |
dc.contributor.author | Wu, Liuhao | - |
dc.contributor.author | Guo, Jiaqi | - |
dc.contributor.author | Yan, Zhongwei | - |
dc.contributor.author | Yoo, Jerald | - |
dc.date.accessioned | 2024-05-03T04:30:17Z | - |
dc.date.available | 2024-05-03T04:30:17Z | - |
dc.date.created | 2024-05-01 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.citation | Digest of Technical Papers - IEEE International Solid-State Circuits Conference, Vol.2023-February, pp.488-490 | - |
dc.identifier.issn | 0193-6530 | - |
dc.identifier.uri | https://hdl.handle.net/10371/200773 | - |
dc.description.abstract | Patient-specific seizure-detection SoCs targeting ambulatory seizure treatment [1-8] achieve outstanding accuracy and low energy consumption for monitoring over an extended period powered by a small battery, energy harvesting or body-coupled powering [9]. However, they must collect each patient's seizure episode EEG to train a classifier before the actual deployment, which requires patients to undergo costly and time-consuming hospitalizations, as there is no guarantee a single hospitalization can capture the event. In contrast, a patient-independent seizure detection can address these issues by training the classifier with pre-existing databases, then directly deploying to new patients (Fig. 32.6.1). Traditional classifiers, such as Logistic Regression (LR), Support Vector Machines (SVM) and Decision Trees (DT) [1-7] are not suitable for patient-independent detection as they have difficulty capturing all the possible seizure patterns across patients without firing too many false alarms; this is due to their computational structures and the nature of inter-patient seizure pattern variation. In contrast, Neural Networks (NN) could perform better by mining the features automatically [8]. However, the inter-patient seizure pattern variation could still be vague to the trained NN. Thus, we present a patient-independent (non-specific) seizure-detection SoC with a Seizure-Cluster-Inception Convolutional Neural Network (SciCNN) to first be trained offline with the pre-existing EEG database for auto feature extraction of the neural patterns, then to be further tuned online for fine calibration after being deployed to an unseen patient. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | SciCNN: A 0-Shot-Retraining Patient-Independent Epilepsy-Tracking SoC | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ISSCC42615.2023.10067518 | - |
dc.citation.journaltitle | Digest of Technical Papers - IEEE International Solid-State Circuits Conference | - |
dc.identifier.scopusid | 2-s2.0-85151714867 | - |
dc.citation.endpage | 490 | - |
dc.citation.startpage | 488 | - |
dc.citation.volume | 2023-February | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yoo, Jerald | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
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- College of Engineering
- Department of Electrical and Computer Engineering
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