Holter系统中运动伪差自动识别的关键技术及算法研究
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摘要
运动干扰是动态心电图(Holter)系统中最为普遍、最难处理的噪声干扰类型,已经成为当前影响动态心电图判读准确性与诊断效率最为主要的因素之一。目前,关于动态心电图运动干扰处理技术的研究主要集中在如何有效地消除运动伪差,而同样关键的运动伪差识别技术以及相关算法却存在许多空白。本论文对Holter系统中运动伪差自动识别的关键技术与算法进行了深入研究,其主要的研究内容和创新成果如下：
1、实现了加速度信号采集模块与Holter记录仪的集成,对不同的健康受试者设计了各种运动实验以探讨加速度信号在运动干扰识别方面的应用。通过在PC上对所采集的三轴加速度信号进行二项式拟合、高通滤波以及均方根求和处理得到运动参考信号(REF)之后,对REF信号进行了光谱映射处理,以辅助临床医生对被检查者的运动时间段进行快速定位。
2、提出了一种新的运动干扰伪差段自动识别算法。通过分析心拍QRS波形态在运动干扰伪差段中的变化规律,基于全程心拍的形态学模板聚类结果,计算了心拍形态无序畸变特征曲线(Consequent Beats Shape Mutation Curve, cSMC),然后在cSMC曲线上进行模糊逻辑决策与局部积分处理,最终得到运动干扰段的起始与终止位置。经MIT-BIH噪声测试数据库验证,算法的准确度为：灵敏度=97.95%,正常心拍误检率=0.91%,室上性心拍误检率=3.33%。此外,在Intel双核2.5G/2GB PC机上,算法对时长约24小时的临床动态心电图的总识别时间小于4秒。
3、提出了结合高阶统计量的运动干扰心拍自动识别算法。对临床上四种常见形态类型的干净心拍加入不同信噪比的运动噪声,通过分析它们的峰度系数变化特征,提出了结合峰度系数与心拍特征的多参数分层次的运动干扰心拍判定策略。在基于心拍模板聚类的运动干扰伪差段识别算法上引入该策略后,整体算法在保证识别精度基本不变的前提下,有效地降低了对正常心拍与室上性心拍的误检率：灵敏度=96.66%,正常心拍误检率=0.19%,室上性心拍误检率=1.74%。
4、结合MIT-BIH噪声测试数据库与临床数据集对本论文的算法进行了验证分析,并按照美国动态心电图机国家标准ANSI/AAMI EC38:1998提出的要求,计算得出了算法的准确度随运动干扰信噪比的变化曲线。实验结果表明,与现有运动干扰伪差识别方法相比较,本论文所提出的运动干扰伪差自动识别算法获得了更高的准确度,并且具有更加高效的运算性能,因此更能适合临床应用。
本论文所提出的运动干扰伪差自动识别算法已经集成至临床Holter系统,并被应用于各种需要区分运动伪差与干净心拍的分析功能中,比如房颤自动检测、心律失常分类、T波电交替分析等等,大大提高了这些分析算法的抗运动干扰性能。
Among all types of noise occur in the Holter system, motion artifact is the most common type and particularly hard to handle. Currently, motion artifact has already become one of the major factors affecting the interpretation accuracy and diagnosis efficiency of the Dynamic Electrocardiogram (DCG). To handle the motion artifacts, previous computational efforts have largely relied on motion artifacts removal, but significantly paid seldom attention to motion artifacts identification up to now. After analyzing the technical limitations of motion artifact removal and the deficiencies of the existing identification techniques, this thesis has done in-depth researches on the key technologies and algorithms for automatic identification of motion artifact in the Holter system.
The main contents and innovations of this thesis mainly include:
1. An acceleration signal acquistion circuit was designed and integrated into the Holter recorders, and a variety of exercise tests were designed for different healthy subjects in order to investigate the application of acceleration signal to motion artifact identification. After processing the collcetced triaxial acceleration signals by binomial fitting, high-pass filter and RMS summation to obtain the motion reference signal (REF), a spectral mapping method was applied to REF to help clinicians quickly locate the examiner's movement periods.
2. A novel method for automatic identification of motion artifact segments was proposed. This thesis first analyzes the QRS complex shape variation law of the beats located in the motion artifact segments, and then calculates the consequent beats shape mutation curve (cSMC) based on the results of beat template clustering. In the cSMC, fuzzy-logic criterion and local integration were employed to find out the start and end position of the motion artifact segments. Using the optimal threshold analysis in the MIT-BIH Noise Stress Test Database, the accuracy of the proposed method is: sensitivity=97.95%. NFR=0.91%and VFR=3.33%. Moreover, the total cost time of our novel method applied to24hours recordings is less than4seconds using the Intel2.5G/2GB PC machine.
3. A combined Higher-order statistics method for motion artifacts identification was proposed. By analyzing the kurtosis variation characteristics of the four common clinical morphological types of heart beats contaminated by the motion artifacts with different signal-to-noise ratio levels, a multi-parameter hierarchical strategy combined kurtosis and beat features was porposed to classify motion artifacts and clean beats. Combine the strategy with the previous motion artifact segment identification method, the NFR and VFR have been effectively reduced while the identification sensitivity keeps alomost unchanged:sensitivity=96.66%, NFR=0.19%and VFR=1.74%.
4. Using the MIT-BIH Noise Stress Test Database and the clinical data gathering from the ECG department of the hospital, the identification performance of the whole method was evaluated, and the accuracy was characterized as a function of signal-to-noise ratio comply with the ANSI/AAMI EC38:1998recommended practice. Compared with the existing identification methods, the results show that the proposed method achieves higher accuracy, and is highly efficient that can be conveniently used in clinical applications.
The proposed method for automatic identification of motion artifacts has been integrated into the clinical Holter system and has been used for many DCG diagnosis applications in need of discrimination between clean and motion artifact contaminated segments, such as atrial fibrillation detection, classification of cardiac arrhythmias and so on.
1、实现了加速度信号采集模块与Holter记录仪的集成,对不同的健康受试者设计了各种运动实验以探讨加速度信号在运动干扰识别方面的应用。通过在PC上对所采集的三轴加速度信号进行二项式拟合、高通滤波以及均方根求和处理得到运动参考信号(REF)之后,对REF信号进行了光谱映射处理,以辅助临床医生对被检查者的运动时间段进行快速定位。
2、提出了一种新的运动干扰伪差段自动识别算法。通过分析心拍QRS波形态在运动干扰伪差段中的变化规律,基于全程心拍的形态学模板聚类结果,计算了心拍形态无序畸变特征曲线(Consequent Beats Shape Mutation Curve, cSMC),然后在cSMC曲线上进行模糊逻辑决策与局部积分处理,最终得到运动干扰段的起始与终止位置。经MIT-BIH噪声测试数据库验证,算法的准确度为：灵敏度=97.95%,正常心拍误检率=0.91%,室上性心拍误检率=3.33%。此外,在Intel双核2.5G/2GB PC机上,算法对时长约24小时的临床动态心电图的总识别时间小于4秒。
3、提出了结合高阶统计量的运动干扰心拍自动识别算法。对临床上四种常见形态类型的干净心拍加入不同信噪比的运动噪声,通过分析它们的峰度系数变化特征,提出了结合峰度系数与心拍特征的多参数分层次的运动干扰心拍判定策略。在基于心拍模板聚类的运动干扰伪差段识别算法上引入该策略后,整体算法在保证识别精度基本不变的前提下,有效地降低了对正常心拍与室上性心拍的误检率：灵敏度=96.66%,正常心拍误检率=0.19%,室上性心拍误检率=1.74%。
4、结合MIT-BIH噪声测试数据库与临床数据集对本论文的算法进行了验证分析,并按照美国动态心电图机国家标准ANSI/AAMI EC38:1998提出的要求,计算得出了算法的准确度随运动干扰信噪比的变化曲线。实验结果表明,与现有运动干扰伪差识别方法相比较,本论文所提出的运动干扰伪差自动识别算法获得了更高的准确度,并且具有更加高效的运算性能,因此更能适合临床应用。
本论文所提出的运动干扰伪差自动识别算法已经集成至临床Holter系统,并被应用于各种需要区分运动伪差与干净心拍的分析功能中,比如房颤自动检测、心律失常分类、T波电交替分析等等,大大提高了这些分析算法的抗运动干扰性能。
Among all types of noise occur in the Holter system, motion artifact is the most common type and particularly hard to handle. Currently, motion artifact has already become one of the major factors affecting the interpretation accuracy and diagnosis efficiency of the Dynamic Electrocardiogram (DCG). To handle the motion artifacts, previous computational efforts have largely relied on motion artifacts removal, but significantly paid seldom attention to motion artifacts identification up to now. After analyzing the technical limitations of motion artifact removal and the deficiencies of the existing identification techniques, this thesis has done in-depth researches on the key technologies and algorithms for automatic identification of motion artifact in the Holter system.
The main contents and innovations of this thesis mainly include:
1. An acceleration signal acquistion circuit was designed and integrated into the Holter recorders, and a variety of exercise tests were designed for different healthy subjects in order to investigate the application of acceleration signal to motion artifact identification. After processing the collcetced triaxial acceleration signals by binomial fitting, high-pass filter and RMS summation to obtain the motion reference signal (REF), a spectral mapping method was applied to REF to help clinicians quickly locate the examiner's movement periods.
2. A novel method for automatic identification of motion artifact segments was proposed. This thesis first analyzes the QRS complex shape variation law of the beats located in the motion artifact segments, and then calculates the consequent beats shape mutation curve (cSMC) based on the results of beat template clustering. In the cSMC, fuzzy-logic criterion and local integration were employed to find out the start and end position of the motion artifact segments. Using the optimal threshold analysis in the MIT-BIH Noise Stress Test Database, the accuracy of the proposed method is: sensitivity=97.95%. NFR=0.91%and VFR=3.33%. Moreover, the total cost time of our novel method applied to24hours recordings is less than4seconds using the Intel2.5G/2GB PC machine.
3. A combined Higher-order statistics method for motion artifacts identification was proposed. By analyzing the kurtosis variation characteristics of the four common clinical morphological types of heart beats contaminated by the motion artifacts with different signal-to-noise ratio levels, a multi-parameter hierarchical strategy combined kurtosis and beat features was porposed to classify motion artifacts and clean beats. Combine the strategy with the previous motion artifact segment identification method, the NFR and VFR have been effectively reduced while the identification sensitivity keeps alomost unchanged:sensitivity=96.66%, NFR=0.19%and VFR=1.74%.
4. Using the MIT-BIH Noise Stress Test Database and the clinical data gathering from the ECG department of the hospital, the identification performance of the whole method was evaluated, and the accuracy was characterized as a function of signal-to-noise ratio comply with the ANSI/AAMI EC38:1998recommended practice. Compared with the existing identification methods, the results show that the proposed method achieves higher accuracy, and is highly efficient that can be conveniently used in clinical applications.
The proposed method for automatic identification of motion artifacts has been integrated into the clinical Holter system and has been used for many DCG diagnosis applications in need of discrimination between clean and motion artifact contaminated segments, such as atrial fibrillation detection, classification of cardiac arrhythmias and so on.
引文
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