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SciCNN: A 0-Shot-Retraining Patient-Independent Epilepsy-Tracking SoC

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dc.contributor.authorTsai, Chne-Wuen-
dc.contributor.authorJiang, Rucheng-
dc.contributor.authorZhang, Lian-
dc.contributor.authorZhang, Miaolin-
dc.contributor.authorWu, Liuhao-
dc.contributor.authorGuo, Jiaqi-
dc.contributor.authorYan, Zhongwei-
dc.contributor.authorYoo, Jerald-
dc.date.accessioned2024-05-03T04:30:17Z-
dc.date.available2024-05-03T04:30:17Z-
dc.date.created2024-05-01-
dc.date.issued2023-02-
dc.identifier.citationDigest of Technical Papers - IEEE International Solid-State Circuits Conference, Vol.2023-February, pp.488-490-
dc.identifier.issn0193-6530-
dc.identifier.urihttps://hdl.handle.net/10371/200773-
dc.description.abstractPatient-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.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleSciCNN: A 0-Shot-Retraining Patient-Independent Epilepsy-Tracking SoC-
dc.typeArticle-
dc.identifier.doi10.1109/ISSCC42615.2023.10067518-
dc.citation.journaltitleDigest of Technical Papers - IEEE International Solid-State Circuits Conference-
dc.identifier.scopusid2-s2.0-85151714867-
dc.citation.endpage490-
dc.citation.startpage488-
dc.citation.volume2023-February-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorYoo, Jerald-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

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