CN106343992B - 心率变异性分析方法、装置及用途 - Google Patents
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Abstract
本发明涉及一种心率变异性(Heart Rate Variability，HRV)分析的方法、装置及用途。利用低成本、便携、可穿戴式信号采集设备获取癫痫患者的术前24小时心电(Electrocardiography，ECG)信号，通过程式化的HRV分析方法计算ECG的多尺度熵(Multiscale Entropy，MSE)，并基于MSE曲线提取表征心率复杂度特征参数，准确、高效地筛选适合迷走神经刺激(Vagus Nerve Stimulation，VNS)手术的药物难治性癫痫患者，避免不必要的经费支出和避免耽误最佳治疗时机，同时通过ECG的MSE复杂度特征参数明确选择VNS手术适应症患者，可以从整体上提高VNS疗法的疗效。
Description
技术领域
本发明涉及一种心率变异性分析方法、装置及用途，尤其涉及在迷走神经刺激(Vagus Nerve Stimulation，VNS)适应症患者的心率变异性分析方法。
背景技术
癫痫作为一种疾病会影响患者的生活，大部分患者可以用一种或多种药物联合治疗对发病加以控制，但仍然会有部分患者对药物治疗并不敏感，这些患者被称为药物难治性癫痫患者。迷走神经刺激(Vagus Nerve Stimulation，VNS)作为一种辅助治疗手段可以有效控制药物难治性癫痫患者的癫痫发作，但其疗效的个体差异非常大，而且具有很高的不确定性。统计分析结果显示，仅有5％-9％接受VNS手术的药物难治性癫痫患者癫痫发作得到完全控制，另有约10％的患者完全无效，其余表现为不同程度的发作频率减少。总体而言，50％-60％接受VNS手术的药物难治性癫痫患者可以达到发作频率减少50％的治疗效果。针对VNS治疗药物难治性癫痫疗效的不确定性和个体差异大的问题，研究人员一直试图通过术前评估筛选出适合VNS手术的患者。
现在尚无明确的VNS手术适应症患者筛选方法应用于临床，而基于脑电(Electroencephalography，EEG)、核磁影像资料(Magnetic Resonance Imaging，MRI)、患者人口统计学特征(性别、年龄等)、临床病史(病程)、发作特征(包括发作类型、发作频率、病灶位置等)进行VNS疗效相关性因素的研究，结论也不相一致，甚至相互矛盾。
现有技术中的思路如附图1所示：药物难治性癫痫患者在接受VNS手术之前首先需进行系统、全面的术前评估(包括人口统计学特征、病史、发作特征、MRI、EEG等)，然后进行VNS植入手术，术后2周左右开机，之后根据每位患者对VNS急性刺激的具体反应情况按照VNS产品的性能、技术特点逐步调整刺激参数并定期统计所有患者发作减少情况。在相应的随访周期(一般为1年)结束后，根据患者发作减少情况即疗效对患者进行分类，最后对不同疗效分类患者的术前评估数据进行统计学分析，寻找不同疗效患者组之间有统计学差异的参数作为VNS手术患者筛选或疗效预测的敏感因子。
现有的EEG和MRI的方法存在成本高、操作复杂和对分析人员专业知识水平要求高的缺点，总体而言，基于上述方法的研究都没有在临床得到应用，主要原因是对同一问题的研究结论不相一致，甚至相互矛盾。本发明提出的基于24小时动态心电信号的方法，仅需要给被试佩戴一个便携式动态心电记录盒，在被试自由活动的状态下采集ECG，操作简单。一般医院门诊对24小时动态心电检查的收费为240元，与长程视频脑电及核磁共振影像检查1000元左右的费用相比，成本相对较低。最为重要的是24小时动态心电采集不受活动限制，与EEG和MRI相比，采集相对简单，一致性较好。
心率变异性(Heart Rate Variability,HRV)是指心电(Electrocardiography，ECG)信号中的相邻心搏间期随时间的变化，它起源于自主神经系统对窦房结自律性的调制，使心搏间期存在几十毫秒甚至更大的差异或波动。HRV蕴含着神经体液调节的大量信息，是目前评价心血管系统自主神经系统活性及其调节功能的定量、无创、可重复指标，通过HRV分析可间接反映交感神经及副交感神经之间的相互作用。
目前还没有利用心电信号HRV分析技术对VNS患者进行筛选的研究和技术方案。由于癫痫疾病与心脏自主神经功能失调有着密切的联系。伴随癫痫的发病和进展，患者心脏自主神经系统平衡被打破，一般表现为交感神经活性增强和迷走神经活性降低。基于这个结论，本发明考虑利用低成本、便携、可穿戴式信号采集设备获取癫痫患者的术前24小时ECG信号，通过程式化的HRV分析方法计算ECG的多尺度熵(Multiscale Entropy，MSE)，并基于MSE曲线提取表征心率复杂度特征参数，准确、高效地筛选适合VNS手术的药物难治性癫痫患者。
发明内容
申请人发现，癫痫疾病与心脏自主神经功能失调有着密切的联系。伴随癫痫的发病和进展，患者心脏自主神经系统平衡被打破，一般表现为交感神经活性增强和迷走神经活性降低。而且，还发现其中心率变异性(Heart Rate Variability,HRV)这一指标非常重要，HRV是指心电(Electrocardiography，ECG)信号中的相邻心搏间期随时间的变化，它起源于自主神经系统对窦房结自律性的调制，使心搏间期存在几十毫秒甚至更大的差异或波动。HRV蕴含着神经体液调节的大量信息，是目前评价心血管系统自主神经系统活性及其调节功能的定量、无创、可重复指标，通过HRV分析可间接反映交感神经及副交感神经之间的相互作用。
基于这一研究发现，本发明利用低成本、便携、可穿戴式信号采集设备获取癫痫患者的术前24小时ECG信号，通过程式化的HRV分析方法计算ECG的MSE，并基于MSE曲线提取表征心率复杂度特征参数，准确、高效地筛选适合VNS手术的药物难治性癫痫患者，避免不必要的经费支出和避免耽误最佳治疗时机，同时通过ECG的MSE心率复杂度特征参数明确选择VNS手术适应症患者，可以从整体上提高VNS疗法的疗效。
本发明提供一种心率变异性分析方法，包括如下步骤：
1)体外采集心电数据；
2)对心电数据进行数字化、去噪处理；
3)将处理后的心电数据形成窦性NN间期序列；
4)选取清醒状态下4个小时的窦性NN间期数据；
5)对清醒状态下4个小时窦性NN间期序列进行MSE计算；
6)通过MSE曲线提取表征心率复杂度的参数。
第5)-6)步的具体方法如下：
i.对步骤4)中得到的NN间期序列{x1,...,xi,...,xN}进行粗粒化处理得到不同尺度的重构序列τ为尺度因子；
ii.针对每个尺度的序列计算样本熵；
iii.将不同尺度因子对应的样本熵绘制曲线，并获取心率复杂度特征参数。
心率复杂度特征参数获取方法如下：
步骤iii中获取的曲线，对尺度1-n1的点进行线性拟合得到斜率Slope n1；对尺度n2-n3进行分段，计算每一段尺度曲线与横坐标构成区域的面积得到参数Areal。
进一步，n1<n2<n3,n3≤40；Areal参数的个数在1～7之间。
进一步，n1＝5,斜率参数Slope为Slope 5，n2＝6、n3＝20，Areal参数为Areal1-5、Areal6-15、Areal6-20。
进一步，Slope参数以及Areal参数进行阈值判断。
进一步，Slope 5的阈值为0.071±0.002，Areal 1-5阈值为4.32±0.04，Areal 6-15
的阈值为10.57±0.2，Areal 6-20的阈值为15.85±0.3。
本发明还提供一种心率变异性分析装置，其特征在于包括心率复杂度计算模块，所述心率复杂度计算模块采用上述的方法进行计算。
进一步，其包括数据采集模块、数字化处理模块、去噪模块、判断模块中的一个或多个。
本发明还提供一种可穿戴心电监测设备，其特征在于包含上述心率变异性分析装置。
附图说明：
图1 是现有技术筛选VNS适应症患者流程图
图2 12导联ECG采集示意图
图3 ECG信号具体处理流程图
图4 MSE分析方法步骤流程图
图5 MSE复杂度指标提取图
图6 阈值选择ROC曲线图
图7 判断流程图
图8 有效组和无效组的MSE曲线
具体实施方式：
实施例1
如图2所示，于术前的24小时标准12导联ECG采集：要求心电采集装置的采样率要大于或等于500Hz，ECG记录期间避免剧烈运动、服药等可能影响心脏功能的活动，记录时间长度为24小时。受试对象、个体的记录环境和条件应基本类似。并要求确保用于HRV分析的数据是正常窦性NN间期。在进行HRV分析时，从24小时长程心电记录中选择4个小时被试清醒状态下的正常窦性NN间期用于MSE分析。ECG信号具体处理流程如图3所示。
1)采集ECG信号，并对信号数字化处理；
2)对数字信号进行去噪声、去伪迹处理；
3)对其中的QRS波自动检测
4)对检测后的信号进行QRS波人工检视；
5)再对异位起搏QRS波信号进行剔除；
6)形成窦性NN间期序列；
7)选取被试清醒状态下4个小时窦性NN间期序列；
8)基于4个小时窦性NN间期序列计算MSE；
9)以尺度因子为横坐标、尺度因子对应的熵值为纵坐标描记MSE曲线；
10)根据MSE曲线提取表征心率复杂度的特征参数。
本发明拟采用的HRV分析中的MSE计算方法，提取表征心率复杂度的Slope5、Area1-5、Area6-15、Area6-20特征参数。
MSE方法步骤(见图4)：
(1)通过被试清醒状态下的4个小时正常窦性NN间期序列{x1,...,xi,...,xN}进行粗粒化处理得到不同尺度的重构序列 τ为尺度因子；
(2)针对每个尺度的序列计算样本熵(Sample Entropy)；
(3)将不同尺度因子对应的样本熵绘制曲线，如图5，通过对尺度1-5的点进行线性拟合得到斜率Slope5、然后分别计算尺度1-5、尺度6-15、和尺度6-20曲线与横坐标构成区域的面积Area1-5、Area6-15、Area6-20，上述4个参数为MSE的心率复杂度特征参数。
对药物难治性癫痫患者进行术前24小时心电采集，采集到的24小时心电数据按上述处理方法得到患者清醒状态下的4个小时正常窦性NN间期序列。对上述4个小时NN间期序列按上述方法进行MSE分析并提取Slope5、Area1-5、Area6-15、Area6-20等表征心率复杂度的特征参数，通过相应的阈值判断进行综合判断选择(如图6所示)，将训练集的VNS手术患者根据术后一段时间随访的疗效进行分类(有效组和无效组)，对有效组和无效组上述心率复杂度指标进行统计学分析并对Slope5、Area1-5、Area6-15、Area6-20指标分 别画受试者工作特征(Receiver Operating Characteristic，ROC)曲线，每个指标的阈值是根据每条曲线上与左上角即坐标(1,1)距离最近的点(尤登指数)，最后根据相应的阈值区分出适合VNS手术的患者和不适合VNS手术的患者(如图7所示)。利用表征心率复杂度的4个特征参数Slope5、Area1-5、Area6-15、Area6-20分别单独进行适合VNS手术和不适合VNS手术患者的区分时，其对应阈值的选择及其相应的筛选准确率如下：
Slope5＝0.071时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为67.9％；
Area1-5＝4.32时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为71.4％；
Area6-15＝10.57时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为92.9％；
Area6-20＝15.85时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为96.4％。
实施例2
实施例1的MSE分析方法中尺度因子扩大到n后的复杂度指标Area6-n指标亦可用于上述VNS患者筛选。
本发明通过药物难治性癫痫患者术前24小时ECG的采集和HRV的MSE分析，可以对药物难治性癫痫患者进行术前筛选，指导不适合VNS疗法的患者接受该手术而选择其他疗法以避免不必要的经费支出和避免耽误最佳治疗时机，同时通过ECG的MSE曲线提取表征心率复杂度的特征参数明确选择VNS手术适应症患者，可以从整体上提高VNS疗法的疗效。
实施例3
根据上述筛选方法，选取于2014年8月13日至2014年12月31日期间在北京天坛医院完成VNS手术的32例药物难治性癫痫患者进行验证。32例药物难治性癫痫患者在VNS术前进行了完整的评估(包括人口统计学特征、临床病史、抗癫痫药物史、24小时视频脑电、MRI以及24小时动态心电等)。
通过术前24小时动态心电数据按上述ECG信号处理方法进行MSE分析，基于每位患者的MSE曲线提取了相应的Slope5、Area1-5、Area6-15、Area6-20特征参数。术 后1年随访结束时，32例接受VNS治疗的药物难治性癫痫患者中，有28例患者有不同程度的发作减少(其中6例患者发作完全控制)视为有效组，其余4例患者发作次数和VNS术前相比没有变化视为无效组。有效组和无效组的MSE曲线如图8所示，两组MSE曲线差别很大，提示通过MSE方法可以进行VNS手术适应症患者的筛选。进一步通过每位患者术前的Slope5、Area1-5、Area6-15、Area6-20特征参数对其疗效进行预测，结果表明上述4个参数中Area6-20预测的最为准确：将其阈值设定为15.85时，28例有效的患者中仅有1例患者的Area6-20＝15.09，如表1所示，其余患者的Area6-20均大于15.85，筛选的准备率超过96％，从而证明上述HRV分析的MSE方法可以准确、有效的进行VNS手术适应症患者的筛选。
表1
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。此外，尽管本说明书中使用了一些特定的术语，但这些术语仅仅是为了方便说明，并不对本发明构成任何限制。
Claims (8)
1.一种心率变异性分析方法，包括如下步骤：
1)体外采集心电数据；
2)对心电数据进行数字化、去噪处理；
3)将处理后的心电数据形成窦性NN间期序列；
4)选取清醒状态下4个小时的窦性NN间期数据；
5)对清醒状态下4个小时窦性NN间期序列进行MSE计算；
6)通过MSE曲线提取表征心率复杂度的参数；
7)为所述参数设定阈值，该阈值用于区分适合VNS手术的药物难治性癫痫患者；
其中第5)-6)步的具体方法如下：
i.对步骤4)中得到的NN间期序列{x1,...,xi,...,xN}进行粗粒化处理得到不同尺度的重构序列1≤j≤N/τ,τ为尺度因子；
ii.针对每个尺度的序列计算样本熵；
iii.将不同尺度因子对应的样本熵绘制曲线，对尺度1-n1的点进行线性拟合得到斜率Slope n1；对尺度n2-n3进行分段，计算每一段尺度曲线与横坐标构成区域的面积得到参数Areal，获取心率复杂度特征参数。
2.如权利要求1所述的心率变异性分析方法，其特征在于：n1<n2<n3,n3≤40；Areal参数的个数在1～7之间。
3.如权利要求1所述的心率变异性分析方法，其特征在于：n1＝5,斜率参数Slope为Slope 5，n2＝6、n3＝20，Areal参数为Areal1-5、Areal6-15、Areal6-20。
4.如权利要求1-3之一所述的心率变异性分析方法，其特征在于：Slope参数以及Areal参数进行阈值判断。
5.如权利要求4所述的心率变异性分析方法，其特征在于：Slope 5的阈值为0.071±0.002，Areal 1-5阈值为4.32±0.04，Areal 6-15的阈值为10.57±0.2，Areal 6-20的阈值为15.85±0.3。
6.一种心率变异性分析装置，其特征在于包括心率复杂度计算模块，所述心率复杂度计算模块采用如权利要求1-5之一所述的方法进行计算。
7.如权利要求6所述心率变异性分析装置，其特征在于还包括数据采集模块、数字化处理模块、去噪模块、判断模块中的一个或多个。
8.一种可穿戴心电监测设备，其特征在于包含权利要求6或7所述的心率变异性分析装置。
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