Seizure-Cluster-Inception CNN (SciCNN): A Patient-Independent Epilepsy Tracking SoC With 0-Shot-Retraining

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS(2023)

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摘要
Epilepsy tracking System-on-Chips (SoC) usually perform patient-specific classification to deal with the patient-to-patient seizure pattern variation from a surface electroencephalogram (EEG). However, the patient-specific classifier training requires the EEG signals from the target patients a priori, which involves costly and time-consuming hospitalization for the inpatient data collection. To address this issue, this paper presents a patient-independent epilepsy tracking SoC that is trained with pre-existing databases and can be directly deployed to the target patients without collecting their data and performing cumbersome patient-specific training beforehand. The proposed SoC adopts a Seizure-Cluster-Inception Convolutional Neural Network (SciCNN) Neural Processor (SNP) to reduce SRAM access rate by 179.05 x with the Kernel-Wise Pipeline (KWP). The 22-Ch. SoC achieves event-based sensitivity of 90.3%/90.4%/83.3% and specificity of 93.6%/95.7%/88.6% on unseen patients from CHB-MIT database/EU database/local hospital patient, respectively.
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关键词
Cross-patient,epilepsy management,hardware,inter-patient variation,intracranial EEG (iEEG),kernel wise pipeline (KWP),neural network,neural processor,non-patient-specific,patient-nonspecific,patient-to-patient variation,seizure classification,seizure detection,surface EEG
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