穿戴式随行监护系统心电信号质量评价研究

穿戴式生理参数监测技术是一种新型的生理监护技术,代表未来监护技术的发展方向,但该类技术应用于临床尚有许多问题亟待解决。本文针对自主研发的穿戴式随行监护系统(SensEcho-5B)的心电信号质量评价问题开展了探索性研究。首先基于模板匹配法开发出一种心电信号质量评价算法,用于心电信号的自动、定量评价,在100名受试者(15名健康人和85名心血管疾病患者)随机抽取的100 h心电信号数据集上进行了算法性能测试。在此基础上使用SensEcho-5B与心电Holter同步采集了30名受试者(7名健康人和23名心血管疾病患者)的24 h心电数据,使用心电信号质量评价算法对两个系统同步记录的心电信号质量进行评价。算法性能测试结果:敏感度为100%,特异度为99.51%,准确率为99.99%。30名受试者的对照试验结果:SensEcho-5B所检测到的心电信号,信号质量较差时间的中位数(Q1,Q3)为8.93(0.84,32.53)min,Holter所检测到的心电信号,信号质量较差时间的中位数(Q1,Q3)为14.75(4.39,35.98)min(秩和检验P=0.133)。研究结果表明,本文提出的心电信号质量评价算法能够对穿戴式随行监护系统的心电信号质量进行有效评价;随行监护系统SensEcho-5B与对照Holter相比,心电信号质量相当。后续研究将进一步在真实临床环境中采集大样本量的随行监护生理数据,并对心电信号质量进行分析和评价,从而使监护系统的性能得到持续优化。     

基于波形特征和SVM的心电信号自动分类方法研究

目的:提出一种新的基于波形特征和SVM的心电信号自动分类实现方法。方法:定义并提取了基于时域特征、小波域特征和高阶统计量特征等三大类心电特征参数,将一次性直接求解多类模式的SVM方法应用于心电信号分类。结果:通过对心电数据库典型心律失常信号的分类测试,验证了所提出心电信号分类方法的有效性。结论:本方法的实现可以有效提高了分类识别精度和速度。     

基于心电-脉搏波的心血管疾病识别研究

为实现心血管疾病的早期筛查,降低心血管疾病临床检测的成本。本研究基于上肢脉搏波传导速度（PWV）及脉搏波相关血液动力学基础理论,采集了总计51人的脉搏波与心电信号数据,提取了包括3种PWV和脉搏波特征参数总计16个特征参数,将不同的PWV与脉搏波特征组成3个样本特征数据集,分别建立了基于K近邻学习（KNN）和支持向量机（SVM）的心血管疾病识别模型。KNN模型分类准确率为66.28%,SVM模型分类准确率为84.3%,并通过对比不同PWV对模型性能的影响,确定了用于血管评估的最优脉搏波传导速度pwvm。研究表明基于SVM建立的分类模型对心血管疾病识别有一定可靠性,为低成本的心血管疾病早期筛查提供了新思路,也为穿戴式心血管系统监测提供了基础。     

基于二维图谱转换的心电信号分类方法

目的 提出一种基于二维图谱转换的心电信号分类方法，以期为临床应用提供参考。方法首先通过格拉姆角场将一维心电信号转换成二维图像，其次对图像提取灰度共生矩阵和颜色矩特征，并运用支持向量机（support vector machines,SVM）对心律失常信号分类，最后使用MIT-BIH公共数据集对此分类器进行训练和测试。结果 该方法分类的总准确率为99.9%，可实现对心律失常的有效分类。结论与传统波形形态分类算法相比，本文提出的将信号转换成二维图谱的分类方法有效解决了模型抗干扰能力差的问题，从而提升了分类器的准确率，具有潜在的临床应用可行性。     

基于支持向量机-隐马尔科夫模型的外骨骼穿戴者步速意图识别

为了提高下肢外骨骼机器人及其穿戴者行走的流畅性和人机相互协调性,本文提出了一种基于惯性传感器信号的下肢外骨骼穿戴者行走步速识别方法。首先选取大腿处和小腿处的三轴加速度和三轴角速度信号,随后根据时间窗口提取当前时刻前0.5 s的信号,以频域信号中的傅里叶变换系数为特征值。接着将支持向量机（SVM）与隐马尔科夫模型（HMM）结合作为分类模型,对该模型进行训练和步速识别。最后结合步速变化规律与人-机约束力,预测当前时刻步速大小。试验结果表明,本文提出的方法能够有效识别下肢外骨骼穿戴者的步速意图,七种步速模式识别率可达到92.14%。本文方法为实现外骨骼与穿戴者之间的人机协调控制提供了新思路和新途径。     

基于连续小波变换和支持向量机的手动想象脑电分类

对左右手运动想象脑电信号进行准确分类是脑-机接口（BCI）研究领域的重要问题。本文利用连续小波变换（CWT）提取脑电信号中相应的手动想象特征信号，并通过支持向量机（SVM）对特征信号进行分类，取得了较好的分类效果，然后经过分析SVM的学习算法，讨论了对于SVM的分类有着关键影响的时间成分，反映出传统的ERD／ERS计算方法可能出现的问题。     

基于形态学特征的下壁心肌梗死检测

下壁心肌梗死的早期检测是降低下壁心肌梗死死亡率的重要手段。针对现有的下壁心肌梗死检测存在模型结构复杂、特征冗余的问题,本文提出一种下壁心肌梗死检测算法。该方法与临床病理信息相结合,提取心电信号Ⅱ、Ⅲ和aVF三个导联中的QRS波段和ST-T波段的峰值、面积以及ST的斜率等特征,并由遗传算法依据单个特征以及特征之间的离散度进行判断,选择区分度较大的特征送入支持向量机(SVM),实现下壁心肌梗死检测。本文采用德国国家计量学研究所(PTB)诊断心电数据库对该方法进行验证,准确率为98.33%。本文方法有效地利用心电信号形态学特征实现了下壁心肌梗死的检测,符合临床医生诊断以及下壁心肌梗死心电信号特异性改变的特点,适合临床广泛推广以及便携式设备应用的实现。     

基于迁移学习的深层卷积神经网络心电信号疲劳分类

传统的心电疲劳分类方法虽然能有效地识别疲劳状态,但需要采集较长时间的信号,不能达到疲劳状态的实时监测。本文设计一种深层卷积神经网络模型用于评估操作员疲劳状态,对操作员的短时心电信号进行疲劳状态的自动分类。首先,提出一种将心电信号转化为图像的方法,将采集到的心电信号转化成二维图像,即将心电信号直接映射到二维空间转换成时域图片信息。然后,将图片送入深层卷积神经网络模型中去训练,实现对操作员疲劳状态的分类。本文方法降低了模型的复杂性,减少了模型的参数,同时训练的数据不需要经过类似噪声滤波、特征提取等任何预处理步骤。结果表明该模型能自动从心电信号中提取有效特征,实现对操作员非疲劳和疲劳两种状态的正确分类,分类准确率达到97.36%。     

基于ABC-SVM的运动想象脑电信号模式分类

为了提高运动想象脑电信号分类的准确率,针对传统支持向量机（SVM）分类方法在脑电信号处理中存在寻优繁 琐、工作量大和分类正确率低等问题,本研究提出一种基于人工蜂群（ABC）算法优化SVM的分类识别方法。首先利用正 则化共空间模式对脑电信号进行特征提取,然后利用ABC算法优化SVM的惩罚因子和核参数,最后利用提取的右手和 右脚两类脑电信号样本特征对优化后的SVM进行训练和分类测试。实验结果表明ABC-SVM分类器提高了脑电信号分 类的准确率,比传统的SVM分类器准确率高出2.5%,证明该算法的可行性和较高准确性。     

基于残差注意力神经网络模型的癫痫脑电信号分类

目的 针对癫痫脑电信号特征提取过程复杂、信息提取不充分及分类精度较低等问题，本文提出一种基于残差注意力神经网络模型（residual attention module neural network,RAM-Net）用于实现癫痫脑电信号的自动分类。方法 首先对脑电信号进行去噪和分段处理，使网络更有效提取细节特征；然后根据脑电信号在时频域幅值特点，将信号转换为二维时频图像作为模型输入；最后借鉴残差网络思想，在每个残差块中融合注意力机制，构建分类模型，在临床数据集上做验证。结果 该方法分类准确率为97.16%，精确率为97.00%，可实现癫痫发作、间期和正常状态的脑电信号三分类。结论 基于RAMNet的癫痫脑电信号分类方法将脑电信号转化为二维图像，降低了方法复杂度；融合注意力机制增强了网络的有效信息提取能力，可为癫痫临床辅助诊断提供一种新的分析思路和处理方法。     

睡眠呼吸暂停综合征患者的睡眠分期算法研究

针对可穿戴睡眠监测缺乏有效的自动睡眠分期和睡眠质量评价方法这一问题,提出一种适用于睡眠呼吸暂停综合征患者的自动睡眠分期方法。通过心电图R-R间期序列,分别得到心率变异性、呼吸幅度变异性和呼吸率变异性信号。以此为基础,提取时域、频域及非线性特征共55个。利用门控循环单元网络,分别构建清醒-睡眠二分类、清醒-快速眼动-非快速眼动睡眠三分类、清醒-快速眼动-浅睡-慢波睡眠四分类、清醒-快速眼动-非快速眼动Ⅰ-Ⅱ-Ⅲ期五分类等共4个不同分类粒度的睡眠分期模型;采用损失函数类别加权方法,有效降低数据非平衡对分期结果的影响。验证数据来自SHRS数据库的274例患者。借助准确率、Cohen's Kappa系数和睡眠结构指标对该睡眠分期方法进行性能评价。结果表明4个分类器的准确率分别为85.06%、75.44%、63.80%、62.13%,Cohen's Kappa系数达到了0.54、0.49、0.41、0.41,睡眠结构分析评估与临床结果之间的差异无统计学意义。所提出的方法基本满足睡眠质量评估的需求,适用于可穿戴睡眠监测应用。     

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Abstract

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)—left arm up down, right arm up down, waist twisting and walking—have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time–frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.     

Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

OBJECTIVE: This paper presents an effective cardiac arrhythmia classification algorithm using the heart rate variability (HRV) signal. The proposed algorithm is based on the generalized discriminant analysis (GDA) feature reduction scheme and the support vector machine (SVM) classifier. METHODOLOGY: Initially 15 different features are extracted from the input HRV signal by means of linear and nonlinear methods. These features are then reduced to only five features by the GDA technique. This not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, the SVM combined with the one-against-all strategy is used to classify the HRV signals. RESULTS: The proposed GDA- and SVM-based cardiac arrhythmia classification algorithm is applied to input HRV signals, obtained from the MIT-BIH arrhythmia database, to discriminate six different types of cardiac arrhythmia. In particular, the HRV signals representing the six different types of arrhythmia classes including normal sinus rhythm, premature ventricular contraction, atrial fibrillation, sick sinus syndrome, ventricular fibrillation and 2 degrees heart block are classified with an accuracy of 98.94%, 98.96%, 98.53%, 98.51%, 100% and 100%, respectively, which are better than any other previously reported results. CONCLUSION: An effective cardiac arrhythmia classification algorithm is presented. A main advantage of the proposed algorithm, compared to the approaches which use the ECG signal itself is the fact that it is completely based on the HRV (R-R interval) signal which can be extracted from even a very noisy ECG signal with a relatively high accuracy. Moreover, the usage of the HRV signal leads to an effective reduction of the processing time, which provides an online arrhythmia classification system. A main drawback of the proposed algorithm is however that some arrhythmia types such as left bundle branch block and right bundle branch block beats cannot be detected using only the features extracted from the HRV signal.     

Characterisation of electrocardiogram signals based on blind source separation

Blind source separation assumes that the acquired signal is composed of a weighted sum of a number of basic components corresponding to a number of limited sources. This work poses the problem of ECG signal diagnosis in the form of a blind source separation problem. In particular, a large number of ECG signals undergo two of the most commonly used blind source separation techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), so that the basic components underlying this complex signal can be identified. Given that such techniques are sensitive to signal shift, a simple transformation is used that computes the magnitude of the Fourier transformation of ECG signals. This allows the phase components corresponding to such shifts to be removed. Using the magnitude of the projection of a given ECG signal onto these basic components as features, it was shown that accurate arrhythmia detection and classification were possible. The proposed strategies were applied to a large number of independent 3s intervals of ECG signals consisting of 320 training samples and 160 test samples from the MIT-BIH database. The samples equally represent five different ECG signal types, including normal, ventricular couplet, ventricular tachycardia, ventricular bigeminy and ventricular fibrillation. The intervals analysed were windowed using either a rectangular or a Hamming window. The methods demonstrated a detection rate of sensitivity 98% at specificity of 100% using nearest neighbour classification of features from ICA and a rectangular window. Lower classification rates were obtained using the same classifier with features from either PCA or ICA and a rectangular window. The results demonstrate the potential of the new method for clinical use.     

Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM

A method based on signal entropy is proposed for the detection of QRS complexes in the 12-lead electrocardiogram (ECG) using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander in the ECG signal. Combined Entropy criterion was used to enhance the QRS complexes. SVM is used as a classifier to delineate QRS and non-QRS regions. The performance of the proposed algorithm was tested using 12-lead real ECG recordings from the standard CSE ECG database. The numerical results indicated that the algorithm achieved 99.93% of detection rate. The percentage of false positive and false negative is 0.54% and 0.06%, respectively. The proposed algorithm performs better as compared with published results of other QRS detectors tested on the same database.     

Fluoroscopic gating without implanted fiducial markers for lung cancer radiotherapy based on support vector machines

Various problems with the current state-of-the-art techniques for gated radiotherapy have prevented this new treatment modality from being widely implemented in clinical routine. These problems are caused mainly by applying various external respiratory surrogates. There might be large uncertainties in deriving the tumor position from external respiratory surrogates. While tracking implanted fiducial markers has sufficient accuracy, this procedure may not be widely accepted due to the risk of pneumothorax. Previously, we have developed a technique to generate gating signals from fluoroscopic images without implanted fiducial markers using template matching methods (Berbeco et al 2005 Phys. Med. Biol. 50 4481-90, Cui et al 2007b Phys. Med. Biol. 52 741-55). In this note, our main contribution is to provide a totally different new view of the gating problem by recasting it as a classification problem. Then, we solve this classification problem by a well-studied powerful classification method called a support vector machine (SVM). Note that the goal of an automated gating tool is to decide when to turn the beam ON or OFF. We treat ON and OFF as the two classes in our classification problem. We create our labeled training data during the patient setup session by utilizing the reference gating signal, manually determined by a radiation oncologist. We then pre-process these labeled training images and build our SVM prediction model. During treatment delivery, fluoroscopic images are continuously acquired, pre-processed and sent as an input to the SVM. Finally, our SVM model will output the predicted labels as gating signals. We test the proposed technique on five sequences of fluoroscopic images from five lung cancer patients against the reference gating signal as ground truth. We compare the performance of the SVM to our previous template matching method (Cui et al 2007b Phys. Med. Biol. 52 741-55). We find that the SVM is slightly more accurate on average (1-3%) than the template matching method, when delivering the target dose. And the average duty cycle is 4-6% longer. Given the very limited patient dataset, we cannot conclude that the SVM is more accurate and efficient than the template matching method. However, our preliminary results show that the SVM is a potentially precise and efficient algorithm for generating gating signals for radiotherapy. This work demonstrates that the gating problem can be considered as a classification problem and solved accordingly.