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Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model
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实时自适应时间序列细分算法的硬件实现,用于皮内植入物.

Gabriel Galeote-Checa, Gabriella Panuccio, Bernabe Linares-Barranco

    IEEE transactions on biomedical circuits and systems
    |October 16, 2025
    PubMed
    概括

    一个新的时间序列分割 (TSS) 算法可以在神经植入物中实现设备内发作检测,从而改善管理. 这种方法减少了数据传输需求和长期使用的功耗.

    科学领域:

    • 生物医学工程 生物医学工程
    • 神经科学是一个神经科学.
    • 信号处理 信号处理

    背景情况:

    • 影响全球超过5000万,许多患者对标准治疗没有反应.
    • 带有设备内算法的神经植入物提供先进的实时发作检测,减少数据传输负担.
    • 植入物中电极数量的增加需要高效的处理来管理数据和电力,以实现长期可行性.

    研究的目的:

    • 引入一种新的时间序列分割 (TSS) 算法,用于从原始神经记录中提取标记信息.
    • 开发一种高效的设备处理系统,用于植入性管理设备.
    • 提高发作检测的准确性,并使局部现场潜能 (LFP) 的临床解释成为可能.

    主要方法:

    • 使用时间序列细分 (TSS) 具有异常值检测和启发式事件分类器.
    • 实施多道共识策略,通过多道协议提高检测准确性.
    • 在微电极阵列 (MEA) 上测试了算法,该算法来自于经过4 - 氨基胺处理的小鼠海马皮层切片的记录.

    主要成果:

    • 该系统实现了高性能发作检测,并将LFP细分为临床相关的标签.
    • 在实验数据上证明了强大的可靠性.
    • 在Pynq-Z2电路板上实现了95%的准确性,94%的灵敏度和0.03%的假阳性率 (FPR),功耗为128兆瓦.

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    结论:

    • 开发的TSS算法在可植入神经设备的设备内处理方面是有效的.
    • 这种方法通过实现高效的实时发作检测,推进了个性化的治疗.
    • 该系统提供了一种低功耗,高精度的解决方案,用于管理先进的神经植入物的日益增长的数据需求.