基于信号质量评估的可穿戴动态心电监护

针对医疗保健领域人体生理监护的需要,提出了一种基于信号质量评估和卡尔曼滤波的可穿戴动态心电监护系统的设计。首先分析了可穿戴动态心电信号的特征,接着给出了基于信号质量评估和卡尔曼滤波的动态心率估计模型,并说明了利用R波检测和加速度计的结果来获得运动状态下心电信号质量指数SQI的方法,然后通过SQI的值对卡尔曼滤波器的参数进行动态调节,以获得最佳的心率估计。最后,通过实际的测试证明了该系统具有较高的可靠性和有效性。     

Formal Analysis of Streaming Downloading Protocol for System Upgrading

For a PC-mobile download system which is embedded with streaming download protocol, there are problems that the data cannot be transmitted correctly from the PC to the mobile, or the transmission is unacceptably slow. To solve these problems, we carry out a formal analysis for the protocol with some timing parameters and a given probability of message loss and unordered data using a probabilistic model checking tool PRISM. We introduce a technique to reduce the state space of the system modeling the protocol which is a network of probabilistic timed automata. The experimental results in PRISM give us a clear explanation to the problems, and are helpful in identifying the optimal parameter settings to meet industrial requirements.     

基于心电图的身份识别方法

针对当前心电图(ECG)身份识别中存在的小样本、多特征点检测问题,提出基于小波变换和动态时间规整(DTW)相结合的方法.利用小波变换对ECG信号进行预处理并提取R波峰值点,提取并保存肢导联QRS波及心拍模板,根据QRS波测试数据与各QRS波模板间的相关性分析以及阈值条件缩小身份识别范围,采用 DTW算法确定心拍测试数据...     

基于深度学习的临床心电图分类算法

心电图反映了人体心脏健康状况,是临床诊断心血管类疾病的重要依据。随着心电图数量的快速增长,计算机辅助心电图分析的需求愈加迫切,心电图自动分类作为实现计算机辅助心电图分析不可或缺的技术手段,具有重要的医学价值。由于心电信号非常微弱、抗干扰性差,传统心电图分类算法存在测试集上效果好,实际临床应用效果欠佳的问题。为此,本文研究一种基于多导联二维结构的一维卷积ResNet网络结构,通过平移起始点、“加噪”等数据增强手段增 加训练样本多样性,并采用Focal Loss损失函数优化病人个体的心电图分类模型。该模型利用2万条完整的8导联心电图数据,共计34类心电异常事件进行分类实验,取得了0.91的F1值、93.96%的准确率和87.89%的召回率的分类性能。实验结果表明,该心电图分类算法模型具有较优的深层特征挖掘与分类能力,验证了其在心电异常自动分类上的有效性。     

Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning

A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.     

Fractional dynamical model for the generation of ECG like signals from filtered coupled Van-der Pol oscillators

In this paper, an incommensurate fractional order (FO) model has been proposed to generate ECG like waveforms. Earlier investigation of ECG like waveform generation is based on two identical Van-der Pol (VdP) family of oscillators, which are coupled by time delays and gains. In this paper, we suitably modify the three state equations corresponding to the nonlinear cross-product of states, time delay coupling of the two oscillators and low-pass filtering, using the concept of fractional derivatives. Our results show that a wide variety of ECG like waveforms can be simulated from the proposed generalized models, characterizing heart conditions under different physiological conditions. Such generalization of the modelling of ECG waveforms may be useful to understand the physiological process behind ECG signal generation in normal and abnormal heart conditions. Along with the proposed FO models, an optimization based approach is also presented to estimate the VdP oscillator parameters for representing a realistic ECG like signal.     

Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

The human heart is a complex system that reveals many clues about its condition in its electrocardiogram (ECG) signal, and ECG supervising is the most important and efficient way of preventing heart attacks. ECG analysis and recognition are both important and tempting topics in modern medical research. The purpose of this paper is to develop an algorithm which investigates kernel method, locally linear embedding (LLE), principal component analysis (PCA), and support vector machine(SVM) algorithms for dimensionality reduction, features extraction, and classification for recognizing and classifying the given ECG signals. In order to do so, a nonlinear dimensionality reduction kernel method based LLE is proposed to reduce the high dimensions of the variational ECG signals, and the principal characteristics of the signals are extracted from the original database by means of the PCA, each signal representing a single and complete heart beat. SVM method is applied to classify the ECG data into several categories of heart diseases. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other ECG recognition techniques, thus indicating a viable and accurate technique.     

Probabilistic generalization of formal concepts

An inductive probabilistic approach to formal concept analysis (FCA) is proposed in which probability on formal contexts is considered; probabilistic formal concepts that have predictive force are defined: nonclassified objects can be assigned to earlier found probabilistic formal concepts; random attributes are eliminated from probabilistic formal concepts; probabilistic formal concepts are robust with respect to data noise. A result of experiment is presented in which formal concepts (in their standard definition in FCA) are first distorted by random noise and then recovered by detecting probabilistic formal concepts.     

Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals

In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyses. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and on the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database compares favourably. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out-performs other well-known approaches for classifying multivariate time series.     

基于AD8232的心电实时监测及分析系统设计

为了方便对患者心电信号进行实时监测,实现对心脏疾病的及时预防及诊断,利用一款基于ATmega328p微控制器的Arduino开发板、一块心电监测前端模块AD8232及上位机软件LabVIEW开发出一套心电实时监测系统,并利用LabVIEW设计出多种软件滤波方法来抑制心电信号中的噪声。由于心电信号的时频特性能提供反映患者心脏活动动态行为的信息,该系统还包括基于LabVIEW设计出的多种用于心电信号实时分析的程序,使被试心电信号所包含的生理特性能够及时地被分析出来。利用所开发的心电实时监测分析系统对被试的心电信号进行采集和分析,发现系统能够非常灵敏、准确地检测心电信号,并对信号噪声有着很好的抑制能力。此外系统能够对信号进行各式的实时分析,且分析结果可靠,能够运用于临床诊断。利用该系统对心电信号进行实时采集和分析,其测量结果准确、去噪效果良好、分析结果可靠,为今后心电实时监测分析系统的设计提供了借鉴。     

小波包分析和形态学滤波相结合的心电图QRS波群定位

心电图波形特征提取是针对-维心电信号的弱信号特征提取.如何排除各种干扰,提取出心电波形特征,并准确定位心电信号中P波、QRS波群、T波,一直是心脏病智能诊断的难点和热点问题,其中QRS波群的定位又是其它波定位的重要依据.利用形态学和小波包理论相结合的方法对这一问题进行了探讨,提出了QRS波群定位和滤除基线漂移的方法.实验证明提出的方法速度较快,能较准确的定位QRS波群、有效的去除基线漂移.     

A comparative analysis of principal component and independent component techniques for electrocardiograms

Principal component analysis (PCA) is used for ECG data compression, denoising and decorrelation of noisy and useful ECG components or signals. In this study, a comparative analysis of independent component analysis (ICA) and PCA for correction of ECG signals is carried out by removing noise and artifacts from various raw ECG data sets. PCA and ICA scatter plots of various chest and augmented ECG leads and their combinations are plotted to examine the varying orientations of the heart signal. In order to qualitatively illustrate the recovery of the shape of the ECG signals with high fidelity using ICA, corrected source signals and extracted independent components are plotted. In this analysis, it is also investigated if difference between the two kurtosis coefficients is positive than on each of the respective channels and if we get a super-Gaussian signal, or a sub-Gaussian signal. The efficacy of the combined PCA–ICA algorithm is verified on six channels V1, V3, V6, AF, AR and AL of 12-channel ECG data. ICA has been utilized for identifying and for removing noise and artifacts from the ECG signals. ECG signals are further corrected by using statistical measures after ICA processing. PCA scatter plots of various ECG leads give different orientations of the same heart information when considered for different combinations of leads by quadrant analysis. The PCA results have been also obtained for different combinations of ECG leads to find correlations between them and demonstrate that there is significant improvement in signal quality, i.e., signal-to-noise ratio is improved. In this paper, the noise sensitivity, specificity and accuracy of the PCA method is evaluated by examining the effect of noise, base-line wander and their combinations on the characteristics of ECG for classification of true and false peaks.     

基于ARM和WIFI技术的心电信号实时检测系统设计与研究

当前心脏疾病是引发我国人民死亡的头号杀手,而传统的心电信号采集系统及终端存在检测数据遗漏、成本高以及适用人群范围窄等问题;该文依据模块化、低能耗、高性能的原则,设计了一种基于ARM和WIFI技术的心电信号实时采集系统;给出了心电信号实时检测系统各功能模块的介绍以及功能实现流程,上位机使用TCP Client作为客户端,MCU作为下位机端,并通过WIFI技术作为通信媒介,设计了主控模块、心电心率采集模块、LCD显示模块以及通信模块,实现了对心电信号的实时采集与传输;系统测试结果验证了该心电信号实时检测系统的实用性、合理性及有效性,实验结果达到预期目标,能够有效提高检测结果准确性,为发现和防治心脏疾病提供一种实时、可靠的检测平台。     

基于模型的嵌入式系统性能分析框架

在现代嵌入式系统中,性能的重要性日益凸显.传统的基于测量的性能分析方法在运行时对性能进行测试,往往在代码实现后才考虑实施.若此时发现的问题是体系结构或设计因素造成的,修复的代价将非常昂贵.提出了一种基于形式化模型的性能分析（formal method-based performance analysis,简称FMPA)方法框架,希望在系统开发的早期,对系统设计模型进行性能分析,以便尽早发现并解决潜在的性能问题.FMPA具有统一的对外接口（UML-MARTE),基于多种形式化模型,并可对多种系统性能指标进行分析.该方法适用于基于模型的开发过程,可为实时嵌入式系统的设计开发提供多项性能指标的参考.通过介绍利用实时模型检测技术分析响应时间与吞吐量,利用概率模型检测技术分析系统可靠性,以及FMPA方法的支撑工具FMPAer的总体设计方案,说明了FMPA方法框架的可行性.     

Formal probabilistic analysis of a surgical robot control algorithm with different virtual fixtures

Surgical robots are increasingly being used in operation theaters involving normal or laparoscopic surgeries. The working of these surgical robots is highly dependent on their control algorithms, which require very rigorous analysis to ensure their correct functionality due to the safety-critical nature of surgeries. Traditionally, safety of control algorithms is ensured by simulations, but they provide incomplete and approximate analysis results due to their inherent sampling-based nature. We propose to use probabilistic model checking, which is a formal verification method, for quantitative analysis, to verify the control algorithms of surgical robots in this paper. As an illustrative example, the paper provides a formal analysis of a virtual fixture control algorithm, implemented in a neuro-surgical robot, using the PRISM model checker. In particular, we provide a formal discrete-time Markov chain-based model of the given control algorithm and its environment. This formal model is then analyzed for multiple virtual fixtures, like cubic, hexagonal and irregular shapes. This verification allowed us to discover new insights about the considered algorithm that allow us to design safer control algorithms.     

Quantitative Analysis With the Probabilistic Model Checker PRISM

Probabilistic model checking is a formal verification technique for establishing the correctness, performance and reliability of systems which exhibit stochastic behaviour. As in conventional verification, a precise mathematical model of a real-life system is constructed first, and, given formal specifications of one or more properties of this system, an analysis of these properties is performed. The exploration of the system model is exhaustive and involves a combination of graph-theoretic algorithms and numerical methods. In this paper, we give a brief overview of the probabilistic model checker PRISM (www.cs.bham.ac.uk/~dxp/prism) implemented at the University of Birmingham. PRISM supports a range of probabilistic models and specification languages based on temporal logic, and has been recently extended with costs and rewards. We describe our experience with using PRISM to analyse a number of case studies from a wide range of application domains. We demonstrate the usefulness of probabilistic model checking techniques in detecting flaws and unusual trends, focusing mainly on the quantitative analysis of a range of best, worst and average-case system characteristics.     

小型化心电信号检测系统设计

为了克服心脏疫病突发就医不及的安全隐患,早期心脏疾病早发现、早治疗,开发了一种基于嵌入式微控制器的小型化心电信号检测系统,该系统包含心电信号采集硬件、控制系统软件以及心率转换算法设计,实现对被测者心电图、呼吸波及心率的获取,利用串口通信方式,上传给串口屏显示模块,并且设计了一套基于心电信号检测的Qt上位机软件系统,可实...     

Taming distributed system complexity through formal patterns

Many distributed systems are real-time, safety-critical systems with strong qualitative and quantitative formal requirements. They often need to be reflective and adaptive, and may be probabilistic in their algorithms and/or their operating environments. All this makes these systems quite complex and therefore hard to design, build and verify. To tame such system complexity, this paper proposes formal patterns, that is, formally specified solutions to frequently occurring distributed system problems that are generic, executable, and come with strong formal guarantees. The semantics of such patterns as theory transformations in rewriting logic is explained; and a representative collection of useful patterns is presented to ground all the key concepts and show their effectiveness.     

Microprocessor-based portable system for heart condition diagnosis

Early diagnosis of heart disease is typically based on a cassette recording of the electrocardiogram (ECG) signal which is then studied and analysed using a microcomputer. The system is bulky, unreliable and prone to mechanical failure. This paper presents the design and implementation of a compact microprocessor-based portable system used for heart condition diagnosis over a long period. The system reads, stores and analyses the ECG signals repetitively in real time for a specified period. The diagnostic data and samples of ECG signals are stored throughout the test period. The system hardware and software design are oriented towards a single-chip microcomputer-based system, hence minimizing size. The operating algorithm is based on a logical approach to ECG signal diagnosis and hence requires little memory.     

An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods

Early diagnosis of prediabetes is an effective solution to the rising cases of diabetes around the world. The heterogeneous physiological characteristics of the ECG signal recorded from the heart make it challenging to implement an efficient diagnostic system. Therefore, this paper proposes a new approach to handling the heterogeneous characteristics of heart rate variability (HRV) with an absolute magnitude deviation analysis and an integrated machine learning technique for prediabetes prediction. We conducted an oral glucose tolerance test to acquire a resting-state ECG signal and the corresponding blood glucose value. We analyzed the HRV pattern from the ECG signal with a block-sliding window technique. We proposed a hybrid model to classify normal and prediabetes based on the extent of the absolute deviation of HRV values and avoiding a single point of failure. We adopted the model from the classification and regression tree (CART) and neural network (NN) algorithms. The experimental results reveal that when the blood glucose level increases, the maximum and range values of CARTHRV decreases while the minimum value increases. The proposed hybrid model had a better performance than the two methods with 100% sensitivity, specificity, and F1-score measures against CART and NN that recorded?<?100% for the same number of prediabetes in the training and test sets. The outcome from the analysis shows that the changes in blood glucose can be observed in ECG signals. The fast approximation of the proposed method to 100% accuracy suggests that it is possible to achieve the diagnosis of prediabetes and overcome the discrepancies in physiological signals among individuals.