CN114403865A - 一种基于神经网络的无创血糖值预测方法 - Google Patents
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Abstract
本发明涉及高通信效率的分布式优化算法技术领域，尤其涉及一种基于神经网络的无创血糖值预测方法，该方法包括：S1、获取医院中大量人体血糖样本数据，构件数据集；S2、收集人体特征数据，作为输入特征；S3、根据S1中人体数据特征，建立层级为五级的全连接神经网络；S4、优化解决算法中过拟合问题；S5、优化数据训练进程，对S2中的样本数据通过对应损失函数确定筛选目标，结合进行准确预测；采用本发明，通过对应的神经网络的训练，对待测者的体温，血压、心率进行多源信息的融合，并在预测过程中，通过多级的优化，防止数据过拟合，并采用SGD优化，适应大批量数据，加速学习与预测进程，提高整体的效率；使得无创预测血样的结果更加准确与稳定。
Description
技术领域
本发明涉及高通信效率的分布式优化算法技术领域，尤其涉及一种基于神经网络的无创血糖值预测方法。
背景技术
无创血糖检测技术，能够帮助使用者连续、无痛苦、无感染隐患地检测血糖水平，具有重要的医学价值和经济价值，多年来始终是研究热点。其中光学传感器检测方案，因使用便捷，成本相对低廉等优势受到研究者较多关注。光电容积波(photoplethysmograph，PPG) 传感器，是通过光电手段检测活体组织中血液容积变化的传感器。该传感器信号交变部分能够反映动脉血液的成份信息，同时信号波形细节能反映血粘稠度、心率变异性、血管壁状态等血液动力学信息，这些信息又与血糖水平具有相关性；
目前无创检测是当今国际学术界研究的热点，而光学法在无创血糖检测技术方面应用较多；拉曼光谱法检测位置是眼前房，导致入射光强受限，还需要解决皮肤的荧光干扰和表层黑色素的影响；偏振光旋光法检测位置为眼睛，存在测量安全性问题，眼前水房糖浓度与血液中血糖浓度具有延迟性；光声光谱法容易受到使用环境和仪器稳定性的干扰，这种干扰比血糖浓度波动引起的干扰更强；荧光法的散射现象对其影响较大，肤色以及皮会影肤厚度响荧光效应；光学相干成像法对生理变化、组织成分变化及运动异常敏感；
并且对于光学法的应用上，血液中各种成分相互影响，基于朗伯比尔定律的校正算法，如多元线性回归、偏最小二乘回归等方法，无法描述人体光谱吸光度的非线性特性，导致对血糖浓度的估计精度无法达到实用的精度标准。
发明内容
本发明目的是针对上述的无创血糖预测方法的局限性，提出一种数据获取样本多样性，提升预测准确性，并且能够大幅度降低数据收集过程与预测计算过程中的时间与经济成本的预测方法。
一种基于神经网络的无创血糖值预测方法，该方法包括以下步骤：
S1、获取医院中大量人体血糖样本数据，构件数据集，大量数据保证模型性能；
S2、收集人体特征数据，作为输入特征；
S3、根据S1中人体数据特征，建立层级为五级的全连接神经网络；
S4、优化解决算法中过拟合问题；
S5、优化数据训练进程，对S1中的样本数据通过对应损失函数确定筛选目标，结合进行准确预测；
进一步的，所述S2中，人体特征数据包括体重、血压、心率和体温；
进一步的，所述S3中的神经网络算法中前四层采用bn函数，并在最后一层添加dropout函数；
进一步的，所述S3中，还采用SGD优化算法，设置学习率为 0.001，并规划得到MESLoss损失函数；
进一步的，所述S2中特征数据包括由双路PPG传感器获取血压收缩压，通过体温传感器快速获取体温数据，通过双路的短波红外进行吸光度参数的获取；
本发明的有益效果是：
采用本发明，通过对应的神经网络的训练，对待测者的体温，血压、心率进行多源信息的融合，并在预测过程中，通过多级的优化，防止数据过拟合，并采用SGD优化，适应大批量数据的，加速学习与预测进程，提高整体的效率；使得无创预测血样的结果更加准确与稳定。
附图说明
图1是本发明整体步骤流程图；
图2是本发明神经网络原理示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。
参考图1-2，一种基于神经网络的无创血糖值预测方法，该方法包括以下步骤：
S1、获取医院中大量人体血糖样本数据，构件数据集，大量数据保证模型性能；
S2、收集人体特征数据，作为输入特征；
S3、根据S1中人体数据特征，建立层级为五级的全连接神经网络；
S4、优化解决算法中过拟合问题；
S5、优化数据训练进程，对S2中的样本数据通过对应损失函数确定筛选目标，结合进行准确预测；
本发明在具体实施过程中：
S1：通过连接医疗系统的数据服务器，获取大量数据血糖指标与其他身体特征的关联数据包，作为神经网络算法的数据集；
S2：收集的人体特征数据为基础的人体特征值，通过对大批量的志愿者进行体重，血压，心率体温的测量，获得生理特征静态数据；其中，通过双路PPG获取人体收缩压，也就是血压数据；两路光电容积脉搏波结合法利用人体两个不同部位，如手指、手腕、耳垂间等。根据测得的两路脉搏波信号特征点的脉搏到达时间差(Pulse Arrive Time Difference，PATD)，通过PATD模型来估算血压；该方法相比 ECG与PPG结合法，在设备复杂度上更加简单，成本较低；并通过体温传感器和体重计更直观快速获取体表特征信息，再通过获取1310和1550两个波段的短波红外吸光度，改短波红外可通过智能穿戴设备进行体外无创的红外吸光度数据采集，如智能手表；
S3：通过设置五个层级的神经网络模型，进行多层级的深度学习，本算法模型，采用多层级感知机制；
S4：S3中的多层级神经网络，整体的X为输入特征，采用损失函数
均方误差(MES):
神经元的输入到输出主体公式为
其中，前四层添加BN函数，BatchNorm就是在深度神经网络训练过程中使得每一层神经网络的输入保持相同分布的；对均值和方差处理后得到
并且在最后一层添加dropout函数，dropout可以作为训练深度神经网络的一种trick供选择；在每个训练批次中，通过忽略一半的特征检测器(让一半的隐层节点值为0)，可以明显地减少过拟合现象。这种方式可以减少特征检测器(隐层节点)间的相互作用，检测器相互作用是指某些检测器依赖其他检测器才能发挥作用；
采用本发明，通过对应的神经网络的训练，对待测者的体温，血压、心率进行多源信息的融合，并在预测过程中，通过多级的优化，防止数据过拟合，并采用SGD优化，适应大批量数据的，加速学习与预测进程，提高整体的效率；使得无创预测血样的结果更加准确与稳定。
采用本发明本文中所描述的具体实施例仅仅是对本发明精神作举例说明；本发明所属技术领域的技术人员可以对所描述的具体实施例做各种各样的修改或补充或采用类似的方式替代，但并不会偏离本发明的精神所定义的范围。
Claims (5)
1.一种基于神经网络的无创血糖值预测方法，其特征在于，该方法包括以下步骤：
S1、获取医院中大量人体血糖样本数据，构件数据集，大量数据保证模型性能；
S2、收集人体特征数据，作为输入特征；
S3、根据S2中人体数据特征，建立层级为五级的全连接神经网络；
S4、优化解决算法中过拟合问题；
S5、优化数据训练进程，对S1中的样本数据通过对应损失函数确定筛选目标，结合进行准确预测。
2.根据权利要求1所述的一种基于神经网络的无创血糖值预测方法，其特征在于，所述S2中，人体特征数据包括体重、血压、心率和体温。
3.根据权利要求2所述的一种基于神经网络的无创血糖值预测方法，其特征在于，所述S4中的神经网络算法中前四层采用bn函数，并在最后一层添加dropout函数。
4.根据权利要求3所述的一种基于神经网络的无创血糖值预测方法，其特征在于，所述S4中，还采用SGD优化算法，设置学习率为0.001，并规划得到MESLoss损失函数。
5.根据权利要求2所述的一种基于神经网络的无创血糖值预测方法，其特征在于，所述S2中特征数据包括由双路PPG传感器获取血压收缩压，通过体温传感器快速获取体温数据，通过双路的短波红外进行吸光度参数的获取。
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| CN116671906A (zh) * | 2023-08-01 | 2023-09-01 | 亿慧云智能科技（深圳）股份有限公司 | 一种智能手表无创血糖测量方法及系统 |
| CN117379016A (zh) * | 2023-12-11 | 2024-01-12 | 吉林省牛人网络科技股份有限公司 | 用于肉牛养殖的远程监控系统及其方法 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116671906A (zh) * | 2023-08-01 | 2023-09-01 | 亿慧云智能科技（深圳）股份有限公司 | 一种智能手表无创血糖测量方法及系统 |
| CN117379016A (zh) * | 2023-12-11 | 2024-01-12 | 吉林省牛人网络科技股份有限公司 | 用于肉牛养殖的远程监控系统及其方法 |
| CN117379016B (zh) * | 2023-12-11 | 2024-02-23 | 吉林省牛人网络科技股份有限公司 | 用于肉牛养殖的远程监控系统及其方法 |
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