用机器学习去解读和解码自发的迷走神经记录
概括
机器学习模型有效地消除来自迷走神经的神经记录,改善神经调节器件的信号提取. 变异性自编码器在保存相关的呼吸活动信号方面表现出卓越的性能.
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.
- 机器学习 机器学习
背景情况:
- 通过神经接口对外围神经进行刺激,有助于治疗和抑郁症等疾病.
- 有效的神经调制需要有效地提取神经数据,通常受到低信号噪声比 (SNR) 和非静止噪声的挑战.
- 机器学习 (ML) 是有前途的,但需要适应生物医学信号处理任务.
研究的目的:
- 调整和评估 ML 算法,用于无监督地消除神经记录的噪音.
- 为了比较基于ML的denoising与传统的波段过渡过用于迷走神经活动.
- 评估ML在保护关闭循环神经调节相关神经特征方面的有效性.
主要方法:
- 应用波段过渡过和两个新的ML算法到in-vivo迷走神经记录.
- 使用一个变化自编码器 (VAE) 进行无监督的无声化.
- 通过提取呼吸道 afferent 活动来比较 denoising 性能.
主要成果:
- 变化自编码器 (VAE) 模型表现出与带程过相比更高的性能.
- 基于VAE的脱显示出与呼吸活动的更好相关性.
- 使用交叉相关性,平均平方误差 (MSE) 和确定呼吸速率的准确性来验证性能.
结论:
- 机器学习算法,特别是VAE,可以有效地消除神经记录,同时保留必要的生物信号.
- 这些ML方法为增强可植入神经调节装置的疗效提供了一个有希望的途径.
- 进一步开发用于神经信号处理的ML对于推进闭环生物医学应用至关重要.
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