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With the enhancement of computing capabilities at edge computing nodes, edge computing based on cloud computing has seen widespread development and application. In the field of epilepsy prevention and treatment, applications related to edge computing have become very popular. Edge computing is a distributed computing model that places data processing, storage, and application functions at the edge location close to the data source, enabling real-time data processing, reducing data transmission latency, and improving system response speed. The temporal complexity of epilepsy electroencephalogram (EEG) signals has been described and characterized at different time periods. Addressing the multi-channel, high-dimensional, and heterogeneous nature of EEG signals and considering their uncertainty and dynamic characteristics, by analyzing and mining foundational data and combining deep learning theories and methods, epilepsy patients’ EEG signals are analyzed and differentiated across multiple time periods, delving into the characteristics of epilepsy EEG signals at different times. In traditional EEG data collection and management systems, cloud storage faces challenges when dealing with large amounts of terminal data, which can reduce real-time data processing performance. To address these issues, this paper proposes an EEG wireless data collection and analysis system based on edge computing. By pushing data processing and storage capabilities to the network edge, data can be processed and analyzed in real-time at the source, thereby reducing data transmission delays and network congestion. Building upon this, the focus is on automatic identification, prediction, and decision-making related to epilepsy, exploring the dynamic patterns of epilepsy automatic identification processes, breaking through traditional epilepsy automatic identification, prediction, and decision-making models, proposing a new method for epilepsy automatic identification, prediction, and decision-making based on “multi-channel fusion - correlation mining - analysis prediction - intelligent decision-making,” and transforming the collected information into diagnosis, prediction, and further control of epilepsy seizures. Clarifying the brain mechanisms during epilepsy seizures through EEG signals can provide more references and insights for epilepsy detection research.

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Research on Automatic Detection and Early Warning of Epilepsy in Electroencephalogram Signals

  • 会议论文

  • 发表日期：

    2025年

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  • Shu-xiong Zheng,
  • Si-tong Li,
  • Hui-lin Zhang,
  • Juan Bao

摘要

With the enhancement of computing capabilities at edge computing nodes, edge computing based on cloud computing has seen widespread development and application. In the field of epilepsy prevention and treatment, applications related to edge computing have become very popular. Edge computing is a distributed computing model that places data processing, storage, and application functions at the edge location close to the data source, enabling real-time data processing, reducing data transmission latency, and improving system response speed. The temporal complexity of epilepsy electroencephalogram (EEG) signals has been described and characterized at different time periods. Addressing the multi-channel, high-dimensional, and heterogeneous nature of EEG signals and considering their uncertainty and dynamic characteristics, by analyzing and mining foundational data and combining deep learning theories and methods, epilepsy patients’ EEG signals are analyzed and differentiated across multiple time periods, delving into the characteristics of epilepsy EEG signals at different times. In traditional EEG data collection and management systems, cloud storage faces challenges when dealing with large amounts of terminal data, which can reduce real-time data processing performance. To address these issues, this paper proposes an EEG wireless data collection and analysis system based on edge computing. By pushing data processing and storage capabilities to the network edge, data can be processed and analyzed in real-time at the source, thereby reducing data transmission delays and network congestion. Building upon this, the focus is on automatic identification, prediction, and decision-making related to epilepsy, exploring the dynamic patterns of epilepsy automatic identification processes, breaking through traditional epilepsy automatic identification, prediction, and decision-making models, proposing a new method for epilepsy automatic identification, prediction, and decision-making based on “multi-channel fusion - correlation mining - analysis prediction - intelligent decision-making,” and transforming the collected information into diagnosis, prediction, and further control of epilepsy seizures. Clarifying the brain mechanisms during epilepsy seizures through EEG signals can provide more references and insights for epilepsy detection research.