CN113576472A - 一种基于全卷积神经网络的血氧信号分割方法 - Google Patents
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Abstract
本发明公开了一种基于全卷积神经网络的血氧信号分割方法,包括以下步骤:步骤1:采集监测信号、人工标记后形成信号数据库;步骤2:采用信号数据库中的数据训练全卷积神经网络;步骤3:将监测信号输入全卷积神经网络即可得到分割结果;本发明方法采用全卷积神经网络从已有的大数据样本中学习,找到最优的超平面去区分氧减和伪差;采用全卷积神经网络可以接收任意长度的输入,更适合血氧信号分割任务。
Description
技术领域
本发明涉及血氧信号分割方法,具体涉及一种基于全卷积神经网络的血氧信号分割方法。
背景技术
随着光电容积脉搏波描记法(Photoplethysmographic PPG)技术的发展,基于PPG技术的可穿戴式设备已经大规模进入大众日常生活。这些设备能够持续的记录佩戴者的PPG信号从而转为血氧指标。设备记录的血氧数据可以在一定程度上反映出佩戴者的健康状况,也可用于筛查睡眠呼吸暂停等疾病。然而由于可穿戴式设备的局限性,生理信号的测量总是会受到运动和设备贴合度的影响,反应在血氧信号上则是出现异常的幅值波动(即伪差)。这种幅值波动可能会被归为氧减,最终导致氧减指数ODI计算出现偏差。如何准确的分辨氧减和伪差,是准确计算ODI的基本条件。
目前常规的伪差监测方法是人为的归纳出一系列的经验条件,使用这些条件去过滤筛选出来的疑似氧减区间,从而实现对伪差的滤除。但是这种方法需要认为的归纳一系列经验条件来判断伪差,而人为设定的经验条件往往存在各种偏差,导致出现伪差的漏检和误检导致监测效率低下、鲁棒性不高。
发明内容
本发明针对现有技术存在的问题提供一种基于全卷积神经网络的血氧信号分割方法。
本发明采用的技术方案是:一种基于全卷积神经网络的血氧信号分割方法,包括以下步骤:
步骤1:采集监测信号、人工标记后形成信号数据库;
步骤2:采用信号数据库中的数据训练全卷积神经网络;
步骤3:将监测信号输入全卷积神经网络即可得到分割结果。
进一步的,所述全卷积神经网络包括编码网络和解码网络;编码网络依次进行四次降采样过程,解码网络依次包括四次上采样过程。
进一步的,所述降采样过程采用降采样模块进行,上采样过程采用上采样模块进行;
降采样模块包括依次串联连接的卷积层、归一化层、激活层和最大池化层;上采样模块包括依次串联连接的卷积层、归一化层和激活层;卷积层输入为编码网络输出和线性差值上采样进行通道叠加后结果。
进一步的,所述步骤3中的监测信号经预处理后输入全卷积神经网络,预处理过程如下:
式中:xi为输入信号,yi为处理后信号,xmean为信号的平均值,xstd为信号的标准差。
进一步的,所述监测信号包括血氧信号、心率信号和体动信号;输出的分割结果(3,L)在第一个维度上经SoftMax归一化后,输出对应类别信号概率,选择概率值最大的类别作为对应点的类别;
式中:xi和xj为输出特征值。
进一步的,所述步骤1标记包括对信号标记氧减和伪差。
进一步的,所述全卷积神经网络采用Dice指标进行评价:
式中:TP为被判定为负样本实际为负样本,FP为被判定为正样本实际为负样本,FN为被判定为负样本实际为正样本。
本发明的有益效果是:
(1)本发明采用全卷积神经网络从已有的大数据样本中学习,找到最优的超平面去区分氧减和伪差;
(2)本发明采用全卷积神经网络可以接收任意长度的输入,更适合血氧信号分割任务;
(3)本发明方法对基于氧减和伪差的Dice指标达到82%和87%,证明了该方法的可行性。
附图说明
图1为本发明方法流程示意图。
图2为本发明中采用的全卷积神经网络结构示意图。
图3为本发明降采样模块和上采样模块的结构示意图。
图4为本发明实施例中的人工标记结果示意图。
图5为本发明实施例中的预测结果示意图。
具体实施方式
下面结合附图和具体实施例对本发明做进一步说明。
如图1所示,一种基于全卷积神经网络的血氧信号分割方法,包括以下步骤:
步骤1:采集监测信号、人工标记后形成信号数据库;监测信号包括血氧信号、心率信号和体动信号。人工标记对信号标记氧减和伪差。
步骤2:采用信号数据库中的数据训练全卷积神经网络;如图2所示,全卷积神经网络包括编码网络和解码网络;其中编码网络(Encoder)负责提取输入信号的特征。解码网络(Decoder)负责将各个层级的特征进行组合和解码操作。图1中左边部分为编码网络。右边部分为解码网络。编码网络依次进行四次降采样过程,解码网络依次包括四次上采样过程。降采样过程采用降采样模块进行,上采样过程采用上采样模块进行。每次采样缩放倍率为2,其中上采样过程中使第一维度减半,在采样结构中,激活函数均采用Relu函数。
结合实际情况,氧减发生通常对应心率变化,而体动则常常产生伪差。因此FCNN网络的输入层维度为(3,L),其中3为通道数、L为信号长度。在经历一系列的卷积操作后,会输出一个对应长度的分割结果向量,该向量的维度为(3,L)。其中输入信号包含了血氧和心率以及体动三种信号,分割结果则对应该点血氧值是{正常值、氧减、伪差}中的哪一类。
如图3所示,降采样模块(图3a)包括依次串联连接的卷积层、归一化层、激活层和最大池化层;上采样模块(图3b)包括依次串联连接的卷积层、归一化层和激活层;卷积层输入为编码网络输出和线性差值上采样进行通道叠加后结果。
训练时,将数据集随机划分为训练集(140例)、验证集(30例)、测试集(30例),模型会在训练集上训练,在验证集上验证模型训练效果,最终选出在验证集上表现最好的模型在测试集验证。
步骤3:将监测信号输入全卷积神经网络即可得到分割结果。
本实施例中,使用云卫康便携式血氧监测仪采集了多人的整夜监测数据,并人工进行了标定和数据清洗,最终筛选出200例可用数据。数据如表1所示。
表1.数据分布
为了使模型能够更好的处理信号,需要将输入信号x做Z-Score标准化处理变换到y。
预处理过程如下:
式中:xi为输入信号,yi为处理后信号,xmean为信号的平均值,xstd为信号的标准差。
输出的分割结果(3,L)在第一个维度上经SoftMax归一化后,输出对应类别信号概率,选择概率值最大的类别作为对应点的类别;
式中:xi和xj为输出特征值。
全卷积神经网络采用Dice指标进行评价:
式中:TP为被判定为负样本实际为负样本,FP为被判定为正样本实际为负样本,FN为被判定为负样本实际为正样本。
图4为人工标记结果示意图,图5为采用本发明方法预测结果示意图。图中Spo2为血氧信号、Hr为心率信号、Move为体动信号。a部分为氧减区间,b部分为伪差区间,从图中可以看出模型能够识别出绝大部分氧减和伪差区间,能够满足实际应用需求。
本发明采用全卷积神经网络从已有的大数据样本中学习,找到最优的超平面去区分氧减和伪差;采用全卷积神经网络可以接收任意长度的输入,更适合血氧信号分割任务;在实施例给出的30例测试样本上,本发明方法对基于氧减和伪差的Dice指标达到82%和87%,证明了该方法的可行性。
Claims (7)
1.一种基于全卷积神经网络的血氧信号分割方法,其特征在于,包括以下步骤:
步骤1:采集监测信号、人工标记后形成信号数据库;
步骤2:采用信号数据库中的数据训练全卷积神经网络;
步骤3:将监测信号输入全卷积神经网络即可得到分割结果。
2.根据权利要求1所述的一种基于全卷积神经网络的血氧信号分割方法,其特征在于,所述全卷积神经网络包括编码网络和解码网络;编码网络依次进行四次降采样过程,解码网络依次包括四次上采样过程。
3.根据权利要求2所述的一种基于全卷积神经网络的血氧信号分割方法,其特征在于,所述降采样过程采用降采样模块进行,上采样过程采用上采样模块进行;
降采样模块包括依次串联连接的卷积层、归一化层、激活层和最大池化层;上采样模块包括依次串联连接的卷积层、归一化层和激活层;卷积层输入为编码网络输出和线性差值上采样进行通道叠加后结果。
6.根据权利要求1所述的一种基于全卷积神经网络的血氧信号分割方法,其特征在于,所述步骤1标记包括对信号标记氧减和伪差。
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CN115670383A (zh) * | 2022-10-31 | 2023-02-03 | 上海跃扬医疗科技有限公司 | 睡眠呼吸暂停事件检测模型的生成方法、检测方法及系统 |
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