基于一维卷积神经网络的房颤智能诊断方法研究

针对“大数据”时代如何利用数据对房颤进行智能、高效的诊断问题,提出了基于一维卷积神经网络的智能诊断方法,以避免传统算法依赖人工特征提取和先验知识的问题。首先,分别构建一维LeNet-5和AlexNet神经网络模型,合理设置网络结构参数;然后,在采集的实验数据基础上针对心电信号的特点进行一系列的数据处理,随机构建训练样本和测试样本;最后,将训练样本分别输入上述2个神经网络模型中训练学习,再将训练好的模型用于房颤的诊断。实验结果表明:一维LeNet-5网络模型存在“过拟合”现象,而一维AlexNet网络模型在避免了上述现象的同时,诊断精度达到了95.34%,较传统方法有了较大提升,为房颤诊断提供了有效的手段。     

ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features

In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution, is introduced. Proposed method consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique.Toward this objective, first, the events of the electrocardiogram (ECG) signal are detected and delineated using a robust wavelet-based algorithm. Then, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Next, the curve length of each excerpted segment is calculated and is used as the element of the feature space. Discrimination power of proposed classifier in isolation of different Gold standard beats was assessed with accuracy 98.20%. Also, proposed learning machine was applied to 7 arrhythmias belonging to 15 different records and accuracy 98.06% was achieved. Comparisons with peer-reviewed studies prove a marginal progress in computerized heart arrhythmia recognition technologies.     

基于香农能量与自适应阈值的心电QRS复合波检测算法

针对现有心电QRS复合波检测算法对于一些信号异常的情况检测效果仍然不理想的问题,提出了一种基于香农能量与自适应阈值相结合的心电QRS复合波检测算法,以解决QRS复合波检测的低准确率问题。首先,从预处理后的信号提取香农能量包络;然后,结合改进的自适应阈值方法对QRS复合波进行检测;最后,根据QRS复合波增强后的信号定位所检测的QRS复合波的位置。使用MIT-BIH心律失常数据库的数据对所提算法进行性能评估,结果表明,所提算法即使在信号中存在高大的P波、T波、不规则心律以及严重的噪声干扰时依然能准确检测QRS复合波的位置,总体数据检测的敏感性、阳性检测度和准确率分别达到了99.88%、99.85%和99.73%,且该算法能够在保证准确率的情况下快速地完成QRS复合波的检测任务。     

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

This study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.     

基于自适应小波阈值的心电信号降噪方法

多尺度分析对于小波阈值的选取以及小波函数的设计依赖性较强,针对不同个体心电信号的降噪效果差异性较大。提出一种自适应的小波阈值计算和选取方法,该方法在启发式阈值优化方法基础上融入了小波分解层数和层级影响因子,通过动态调整每一层小波系数的阈值计算函数实现更加合理的信号分解与降噪处理。实验结果表明所提出算法在心电信号降噪效果方面获得了较好的表现,能够满足临床应用需求。     

基于改进EEMD的心电信号基线漂移消除方法

针对传统方法滤波效果不佳的问题,本文提出了基于改进集合经验模态分解（Ensemble empirical mode decomposition,EEMD）的消除心电信号基线漂移方法。该方法克服了经验模态分解（Empirical mode decomposition,EMD）模态混叠的问题,并对EEMD方法存在的问题和不足进行改进,建立集合经验模态分解方法中加入辅助白噪声大小的可依据准则,从而确定加入的辅助白噪声大小以及集合平均次数这两个重要参数。它从含噪心电信号中提取基线漂移信号,然后重构其余本征模函数（Intrinsic mode function,IMF）分量得到"干净"的心电信号,为后续的研究提供前提。经实验验证表明：相较于传统方法,这种方法能够提高信噪比、降低均方差、保持特征波形、去噪更加彻底,很好地解决了心电信号低频成分损失的问题。     

房颤智能检测中的心电特征选择和机器学习

房颤是最常见的心律失常疾病,因其临床诊断率低而促进了实时自动检测算法的发展。但大多算法缺乏模型或数据库间的对比,难以评判模型的优劣。为此考虑选择3种机器学习算法(支持向量机、随机森林、逻辑回归)构建3个独立的房颤检测模型。3种模型分别在MIT-BIH房颤数据库上训练,并在3个独立数据库上进行测试和对比,同时进一步分析特征选择对模型性能的影响。结果表明选择12个特征(3个时域特征和9个非线性特征)时,3种模型在2018年中国生理挑战赛公开数据集和可穿戴式动态心电数据集上的灵敏度、特异度、准确性和F1分数均达到95%以上,且随机森林相较于另两种算法具有更强的稳定性和泛化能力。     

老年冠状动脉粥样硬化性心脏病(冠心病)患者心律失常和心肌缺血诊断中动态心电图的临床价值

目的:探究动态心电图在老年冠状动脉粥样硬化性心脏病(冠心病)患者心律失常和心肌缺血诊断中的临床应用价值。方法:选择2018年6月至2020年6月在我院接受诊疗的70例冠状动脉粥样硬化性心脏病患者作为研究对象,随机将患者分成两组,35例对照组患者接受常规心电图检查,另外35例观察组患者接受动态心电图检查。对比两组患者心律失常以及心肌缺血检出率。结果:两组心律失常检出率比较,观察组在心房颤动、房性早搏、室性早搏、房室传导阻滞检出率上均高于对照组,数据对比差异存在统计学意义,P<0.05。观察组心肌缺血的检出率为94.29%,明显高于对照组77.14%,数据差异显著,存在统计学意义,P<0.05。结论:在老年冠状动脉粥样硬化性心脏病(冠心病)患者心律失常和心肌缺血诊断中应用动态心电图可以获得更高的检出率,比较准确地反映出患者心律失常以及心肌缺血的表现,有助于明确病情以及疾病诊断,为疾病临床诊断提供可靠依据。     

基于GAN-CNN的心律失常识别

心电图分析是医生诊断心律失常的重要依据.对心律失常的准确判断有助于患者及时了解身体状况并发现潜在疾病.然而,心电图分析不仅费时费力,而且还依赖于临床经验,因此心电图分析的效率一直受到医生数量和工作效率的限制.深度学习技术的发展为计算机辅助诊断系统的开发提供了基础.文中将一维心电信号转换为二维灰度图像,并采用一种GAN-...     

脑电波、心电图的测量

我们采用自制的放大器对心电信号进行测量，并且使用DEWE－3010虚拟数字示波器、DASYLAB数据采集和信号处理软件、Excel软件来完成信号采集、信号滤和信号处理等工作，并进行了相关的误差分析，绘制出了完整的心电图，并为测量脑电波信号打下了坚实的基础。