CN111317473A - 基于混合测量技术的血糖检测方法 - Google Patents
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Abstract
本发明公开了一种基于混合测量技术的血糖检测方法,根据获取施加在测量位置上的双电极或四电极测量得到的电压值和电流值,计算得到电阻抗值,并建立对应的生物电阻抗谱,同时在对应位置上获取对应的血糖值和人体成分数值,并根据所述生物电阻抗谱和所述人体成分数值进行数据清洗和特征筛选,将筛选后的数据进行归一化和人工智能学习后,得到准确的血糖模型,提高了血糖测量的准确性。
Description
技术领域
本发明涉及生物医学工程技术领域,尤其涉及一种基于混合测量技术的血糖检测方法。
背景技术
糖尿病又称之为高血糖症,是世界范围内的流行病。目前常用的血糖检测方法多为有创或微创方法,不仅给患者造成疼痛,同时增加了感染风险,限制了血糖浓度检测的次数和效果。而无创血糖检测可以实现无痛,无风险,低成本和多次血糖检测,是目前研究热点,无创血糖检测方法主要包括反向离子电渗方法,代谢热整合法,光学相关方法。由于人体组成的复杂性和生理过程的多干预性,迄今为止,并没有仅通过单一的非侵入性方法,或是仅通过一种测量方法就可以达到良好的临床测试结果。
近年来,国外多个研究机构将生物阻抗检测技术与无创血糖测试相结合,开辟了无创血糖检测的新方向。生物电阻抗检测具有舒适、简单、快捷、成本低等优点。然而由于人体内环境非常复杂,不同人间的身体差异性也非常大,采用生物电阻抗方法测量人体血糖时,人体皮肤的粗糙程度,体内的水分含量等都会降低血糖测量的准确性。
发明内容
本发明的目的在于提供一种基于混合测量技术的血糖检测方法,提高血糖测量准确性。
为实现上述目的,本发明提供了一种基于混合测量技术的血糖检测方法,包括:
获取测量位置的电阻抗值,建立对应的生物电阻抗谱;
获取对应的血糖值和人体成分数值;
对所述生物电阻抗谱和所述人体成分数值进行清洗和特征筛选;
完成筛选后,进行归一化和人工智能学习,得到血糖模型。
其中,所述获取测量位置的电阻抗值,建立对应的生物电阻抗谱,包括:
根据获取施加在测量位置上的双电极或四电极测量得到的电压值和电流值,计算得到对应的电阻抗值,并根据所述电阻抗值建立对应的生物电阻抗谱。
其中,所述获取对应的血糖值和人体成分数值,包括:
基于皮肤等效模型,获取所述测量位置对应的血糖值和对应的人体成分数值,其中,所述人体成分包括包括无机盐、蛋白质、细胞内水分、细胞外水分、细胞总水分、骨骼肌、去脂体重和体脂肪。
其中,对所述生物电阻抗谱和所述人体成分数值进行数据清洗和特征筛选,包括:
根据所述生物电阻抗谱和所述人体成分数值,清除重复、错误异常和偏离整体分布的数据,并按照设定要求对所述生物电阻抗谱和所述人体成分数值进行特征筛选。
其中,所述完成筛选后,进行归一化和人工智能学习,得到血糖模型,包括:
将完成筛选后的所述生物电阻抗谱和所述人体成分数值与设定值之间的第一差值和阈值与设定值之间的第二差值做除法,并将得到的商值利用人工智能学习法,得到准确的血糖模型。
本发明的一种基于混合测量技术的血糖检测方法,根据获取施加在测量位置上的双电极或四电极测量得到的电压值和电流值,计算得到电阻抗值,并建立对应的生物电阻抗谱,同时在对应位置上获取对应的血糖值和人体成分数值,并根据所述生物电阻抗谱和所述人体成分数值进行数据清洗和特征筛选,将筛选后的数据进行归一化和人工智能学习后,得到准确的血糖模型,提高了血糖测量的准确性。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1是本发明提供的一种基于混合测量技术的血糖检测方法的步骤示意图。
图2是本发明提供的四电极与双电极测量原理图。
图3是本发明提供的皮肤阻抗等效电路。
图4是本发明提供的皮肤等效模型。
具体实施方式
下面详细描述本发明的实施例,所述实施例的示例在附图中示出,其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的,旨在用于解释本发明,而不能理解为对本发明的限制。
请参阅图1,本发明提供一种基于混合测量技术的血糖检测方法,包括:
S101、获取测量位置的电阻抗值,建立对应的生物电阻抗谱。
具体的,根据获取施加在测量位置上的双电极或四电极测量得到的电压值和电流值,计算得到对应的电阻抗值,并根据所述电阻抗值建立对应的生物电阻抗谱,并且会采集不同受测者足够多的次数的生物电阻抗谱,提高准确性,其中,双电极或四电极测量技术是将幅值恒定的交变电流通过一对电极接触受测者的组织,再通过另一对电极将受测部位两端的电压检测出,电路连接如图2所示,具体为,在测量时电流从贴近皮肤的一对电极片流进,依次经过皮肤、皮下组织、另一边皮下组织以及另一边皮肤,最后从另一对电极片流出,皮肤的等效阻抗电路如图3所示,因此人体皮肤等效阻抗为皮肤阻抗和皮肤下其他组织阻抗之和的形式体现。若采集到电压值为V∠θ1,电流值为I∠θ2,得到电阻抗为:
S102、获取对应的血糖值和人体成分数值。
具体的,皮肤可分为表皮层、真皮层及皮下组织,皮肤的最外层是表皮层,包含有角质层,导电性能极差,可以类比为电介质形式,表皮层下是真皮层及皮下组织,其中含有大量的血管,因此导电性能较好,可以模拟为纯电阻形式,表皮层主要由角质形成细胞、黑素细胞、朗格汉斯细胞和梅克尔细胞组成,真皮层主要由胶原蛋白、弹性纤维以及水分构成,皮下组织是由疏松结缔组织和脂肪组织组成,皮肤等效模型如图4所示。当对不同受测者通入高频的交变电流时,皮肤表皮电阻基本恒定,而不同人群间的皮下组织差异性较大,所以在利用血糖所造成的电解质阻抗的变化来检测血糖的浓度时,由于上述的皮下组织差异性所导致的生物电阻抗波动属于人体各异性的体现,并不是血糖所造成的电解质阻抗的波动变化,因此基于所述皮肤等效模型,获取所述测量位置对应的血糖值和对应的人体成分数值,其中,所述人体成分包括包括无机盐、蛋白质、细胞内水分、细胞外水分、细胞总水分、骨骼肌、去脂体重和体脂肪,将测量得到的人体成分数据与血糖值结合对比分析,并且采集不同受测者足够多的次数的准确血糖值以及该段测量位置的人体成分,降低人体各异性的体现,提高测量准确性。
S103、对所述生物电阻抗谱和所述人体成分数值进行数据清洗和特征筛选。
具体的,通过人体成分测量的相关项目中,能得到受测者的一定皮肤质量指标,该指标会依据测试人员的不同,体现为具体数值的不同,以此实现对人体差异性的区分与修正,根据所述生物电阻抗谱和所述人体成分数值,清除重复、疑似错误异常和偏离整体分布的数据,并按照设定要求对所述生物电阻抗谱和所述人体成分数值进行特征筛选,可避免不需要的数据对血糖测量结果产生影响,降低测量的准确性。
S104、完成筛选后,进行归一化和人工智能学习,得到血糖模型。
具体的,将完成筛选后的所述生物电阻抗谱和所述人体成分数值与设定值之间的第一差值和阈值与设定值之间的第二差值做除法,即将任意数据减去特征筛选后的最小值的第一差值除以最大数据值减去最小值的第二差值,使所有数据映射到(0,1)之间,并将得到的商值利用人工智能学习法,得到准确的血糖模型,其中,所述人工智能学习法包括随机森林、深度学习、多重神经网络等方式,可以得到准确的血糖模型,例如采用多重神经网络的方法,将归一化后的数据输入到神经网络中,经过多层卷积层的特征提取后,经由输出层输出最后的血糖值,去除了皮下组织差异性所导致的部分数据波动,进而得到了仅由血糖变化所引起电阻抗变化的阻抗谱。将生物电阻抗谱技术与人体成分检测技术相结合,实现了对人体皮下组织差异性问题的分析与解决,进而实现更为有效准确的血糖测量。
本发明的一种基于混合测量技术的血糖检测方法,根据施加在测量位置上的双电极或四电极测量得到的电压值和电流值,计算得到电阻抗值,并建立对应的生物电阻抗谱,同时在对应位置上获取对应的血糖值和人体成分数值,并根据所述生物电阻抗谱和所述人体成分数值进行清洗和特征筛选,将筛选后的数据进行归一化和人工智能学习后,得到准确的血糖模型,提高了血糖测量的准确性。
以上所揭露的仅为本发明一种较佳实施例而已,当然不能以此来限定本发明之权利范围,本领域普通技术人员可以理解实现上述实施例的全部或部分流程,并依本发明权利要求所作的等同变化,仍属于发明所涵盖的范围。
Claims (5)
1.一种基于混合测量技术的血糖检测方法,其特征在于,包括:
获取测量位置的电阻抗值,建立对应的生物电阻抗谱;
获取对应的血糖值和人体成分数值;
对所述生物电阻抗谱和所述人体成分数值进行数据清洗和特征筛选;
完成筛选后,进行归一化和人工智能学习,得到血糖模型。
2.如权利要求1所述的一种基于混合测量技术的血糖检测方法,其特征在于,所述获取测量位置的电阻抗值,建立对应的生物电阻抗谱,包括:
根据获取施加在测量位置上的双电极或四电极测量得到的电压值和电流值,计算得到对应的电阻抗值,并根据所述电阻抗值建立对应的生物电阻抗谱。
3.如权利要求1所述的一种基于混合测量技术的血糖检测方法,其特征在于,所述获取对应的血糖值和人体成分数值,包括:
基于皮肤等效模型,获取所述测量位置对应的血糖值和对应的人体成分数值,其中,所述人体成分包括包括无机盐、蛋白质、细胞内水分、细胞外水分、细胞总水分、骨骼肌、去脂体重和体脂肪。
4.如权利要求3所述的一种基于混合测量技术的血糖检测方法,其特征在于,对所述生物电阻抗谱和所述人体成分数值进行数据清洗和特征筛选,包括:
根据所述生物电阻抗谱和所述人体成分数值,清除重复、错误异常和偏离整体分布的数据,并按照设定要求对所述生物电阻抗谱和所述人体成分数值进行特征筛选。
5.如权利要求4所述的一种基于混合测量技术的血糖检测方法,其特征在于,所述完成筛选后,进行归一化和人工智能学习,得到血糖模型,包括:
将完成筛选后的所述生物电阻抗谱和所述人体成分数值与设定值之间的第一差值和阈值与设定值之间的第二差值做除法,并将得到的商值利用人工智能学习法,得到准确的血糖模型。
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