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<p>Personalized seizure forecasting with wearable electroencephalography (EEG) is limited by sparse patient-specific labels, privacy constraints and finite device and gateway resources. This study evaluates a generative-digital-twin and meta-learning workload as an Edge–Twin/Fog–Cloud service for 24-hour personalized forecasting. In this design, warning inference remains on the wearable, patient-state tracking and synthetic support-set construction run at a nearby Twin/Fog node, and cross-patient meta-parameter management remains in the Cloud. Using a trace-driven simulator derived from the Children’s Hospital Boston–Massachusetts Institute of Technology (CHB-MIT) dataset, we quantify the communication and on-device adaptation needed to establish and maintain patient state. Under steady monitoring, sparse uploads reduce shared-gateway bandwidth demand by 98.3% relative to continuous streaming. During cold start, uploading six EEG segments per hour provides the required labeled support windows and a personalized warning within one hour while retaining 91.4% sensitivity. Reducing wearable-side adaptation from four fine-tuning updates to one leaves 87.8% sensitivity, which is treated as a fallback level. For refresh, event-triggered Twin/Fog dispatch preserves 95.0% day-average sensitivity at 13.0 Mbits/day, compared with 95.1% at 720.0 Mbits/day for hourly refreshing and 91.5% at 4.3 Mbits/day for weekly refreshing. The results support a state-dependent operating rule: spend communication and adaptation when patient state is being established or has drifted, then reduce upload and refresh once the state is stable.</p>

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An edge–cloud continuum service for personalized seizure forecasting: communication-efficient digital twins with few-shot updates

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Menglong Li,
  • Weiqiang Zhang,
  • Yujie Zhang,
  • Quanquan Liu,
  • Wei Xing

摘要

Personalized seizure forecasting with wearable electroencephalography (EEG) is limited by sparse patient-specific labels, privacy constraints and finite device and gateway resources. This study evaluates a generative-digital-twin and meta-learning workload as an Edge–Twin/Fog–Cloud service for 24-hour personalized forecasting. In this design, warning inference remains on the wearable, patient-state tracking and synthetic support-set construction run at a nearby Twin/Fog node, and cross-patient meta-parameter management remains in the Cloud. Using a trace-driven simulator derived from the Children’s Hospital Boston–Massachusetts Institute of Technology (CHB-MIT) dataset, we quantify the communication and on-device adaptation needed to establish and maintain patient state. Under steady monitoring, sparse uploads reduce shared-gateway bandwidth demand by 98.3% relative to continuous streaming. During cold start, uploading six EEG segments per hour provides the required labeled support windows and a personalized warning within one hour while retaining 91.4% sensitivity. Reducing wearable-side adaptation from four fine-tuning updates to one leaves 87.8% sensitivity, which is treated as a fallback level. For refresh, event-triggered Twin/Fog dispatch preserves 95.0% day-average sensitivity at 13.0 Mbits/day, compared with 95.1% at 720.0 Mbits/day for hourly refreshing and 91.5% at 4.3 Mbits/day for weekly refreshing. The results support a state-dependent operating rule: spend communication and adaptation when patient state is being established or has drifted, then reduce upload and refresh once the state is stable.