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基于光聚缩图的非侵入性血糖估计,使用静缩-放缩框架MFCC特征和机器学习回归

Ali Kermani¹, Hossein Esmaeili²

¹Electrical & Computer Engineering Department, University of Science & Technology of Mazandaran, Behshahr, Iran.

BioImpacts : BI

|September 9, 2025

概括

这项研究引入了一种新型的非侵入性血糖估计方法,使用从光聚光学 (PPG) 信号的梅尔频率塞普斯特拉系数 (SDFMFCC) 的静缩-透缩框架. SDFMFCC方法实现了高精度,为传统的侵入性葡萄糖监测提供了有希望的替代方案.

科学领域:

生物医学工程生物医学工程
信号处理信号处理
医学诊断医学诊断医学诊断

背景情况:

准确的血糖估计对于管理糖尿病和整体健康至关重要.
传统的血糖监测方法是侵入性的,造成不适并减少患者的坚持.
显著需要可靠的,非侵入性的血糖估计技术.

研究的目的:

开发和验证一种用于估计血糖的新型非侵入性方法.
提高使用光电显微镜 (PPG) 信号估计葡萄糖的准确性和可靠性.
引入缩 - 扩张的框架Mel频率的 cepstral 系数 (SDFMFCC) 作为一个先进的特征提取技术.

主要方法:

采用静心 - 放心框架的特征提取Mel频率塞普斯特拉系数 (SDFMFCC).
通过Savitzky-Golay过器和局部极端检测识别静缩和静缩点.
使用支持向量回归 (SVR) 对23名志愿者的PPG信号进行血糖水平估计.

主要成果:

在血糖估计方面取得了高准确性 (99.8%) 和精度 (0.996).
报告了26.01 mg/dL的根平均平方误差 (RMSE).
克拉克错误网分析显示,99.273%的预测在临床上可接受的A区内.

结论:

SDFMFCC方法显著提高了非侵入性血糖估计的准确性和可靠性.
这种新的框架技术有效地捕捉了PPG信号的生理变化.
SDFMFCC为侵入性血糖监测方法提供了一个可行的和有前途的替代方案.

关键词:

在MFCC中,MFCC是最重要的.机器学习回归回归非侵入性的葡萄糖估计.这是一个PPGPPG.缩 - 放缩 - 放缩的时间.

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