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贝叶斯无声算法处理连续血糖监测时间序列中的彩色,非静止噪声

Nunzio Camerlingo¹, Ilaria Siviero², Martina Vettoretti¹

¹Department of Information Engineering, University of Padova, Padova, Italy.

Frontiers in bioengineering and biotechnology

|December 11, 2023

概括

一个新的贝叶斯无声化 (BD) 算法通过有效地消除彩色和非静止噪声来改进持续葡萄糖监测 (CGM) 数据分析. 这种方法提高了CGM追溯时间序列分析的准确性.

科学领域:

生物医学工程生物医学工程
数据科学数据科学数据科学
信号处理信号处理

背景情况:

持续血糖监测 (CGM) 时间序列数据的回顾性分析经常受到测量噪声的挑战.
这种噪音可以是有色的 (自相关) 和非静止的 (时间变化的变异),使准确的解释变得复杂.
现有的方法可能无法同时充分解决两种噪声特征.

研究的目的:

介绍和评估一个新的贝叶斯解密 (BD) 算法.
解决测量噪声的自相关性和其在CGM数据中差异的时间变化.
为了提高追溯CGM时间序列分析的准确性.

主要方法:

该BD算法采用适应性,对信号和噪声的先验模型.
使用部分重叠的CGM窗口和线性平均平方估计平滑方法估计未知方差.
核心光滑用于重建CGM信号和噪声变化概况.
该算法在模拟数据集 (D_S1,D_S2) 和真实Dexcom G6 CGM数据 (D_R) 上进行了验证.

主要成果:

在模拟数据D_S1上,BD将根平均平方误差 (RMSE) 从8.10降至6.28 mg/dL,优于彩色噪声的文献算法.
在模拟数据D_S2上,BD将RMSE从6.85降低到5.35 mg/dL,与Butterworth波器相比,有效处理噪声差异时间变化.
对于真实CGM数据 (D_R),BD证明了噪声差异变量的合理跟踪和令人满意的无声化性能.

结论:

开发的贝叶斯无效化 (BD) 算法有效地解决了CGM测量误差的固有特征.
在处理CGM数据中的彩色和非静止噪声方面,BD显著优于现有的消除噪声算法.
这一进步有望对CGM时间序列数据进行更准确的回顾性分析.

关键词:

贝叶斯主义者拒绝贝叶斯主义者.巴特沃思过器的过器不断监测葡萄糖的情况.相关性相关性相关性静止性是一种静止性.

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