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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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采用边缘启用,使用几何深度学习预测前-ictal活动框架.

Humza F Abbasi, Faizan Hamayat, Rana F Ahmad

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们使用大脑网络转换器 (BNT) 开发了一个边缘启用框架,通过EEG信号准确预测发作. 该系统提供实时分析和高性能,为改善患者干预铺平了道路.

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    科学领域:

    • 神经科学是一个神经科学.
    • 人工智能的人工智能
    • 医疗技术 医疗技术 医学技术

    背景情况:

    • 发作对患者的管理和生活质量构成重大挑战.
    • 准确和及时预测前发作状态对于有效的干预至关重要.
    • 现有的用于预测的深度学习技术往往缺乏可解释性和实时边缘部署能力.

    研究的目的:

    • 引入一个边缘启用发作前期预测框架.
    • 评估大脑网络变压器 (BNT) 的性能,以分析基于EEG的大脑连接.
    • 为了证明BNT对边缘设备实时部署的适用性.

    主要方法:

    • 利用大脑网络变压器 (BNT) 模型来分析电脑电图 (EEG) 信号.
    • 采用几何深度学习技术,专注于基于EEG的大脑连接.
    • 将BNT性能与CHB-MIT数据集上的各种现有深度学习技术进行比较.
    • 在Nvidia Jetson Xavier NX边缘设备上的基准计算效率.

    主要成果:

    • 在CHB-MIT数据集中,BNT获得了高精度 (平均97.17%,中位数98.51%) 和AUC分数为0.99.
    • 该模型在边缘设备上展示了9.978ms的平均推断时间,表明实时能力.
    • 在触发前活动预测方面,BNT的表现优于现有的技术.

    结论:

    • BNT框架为实时发作预测提供了一个准确,可解释和计算高效的解决方案.
    • 边缘设备兼容性使该系统适合在临床和可穿戴监控系统中实际部署.
    • 这项技术有可能通过及时干预和提高生活质量,显著改善患者的治疗结果.