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。     

Evaluating cardiac health through semantic soft computing techniques

Heart Rate Variability (HRV) represents a physiological phenomenon which consists in the oscillation in the interval between consecutive heartbeats. Based on the HRV analysis, cardiology experts can make a assessment for both the cardiac health and the condition of the autonomic nervous system that is responsible for controlling heart activity and, consequently, they try to prevent cardiovascular mortality. In this scenario, one of the most widely accepted and low-cost diagnostic procedures useful for deriving and evaluating the HRV is surely the electrocardiogram (ECG), i.e., a transthoracic interpretation of the electrical activity of the heart over a period of time. With the advent of modern signal processing techniques, the diagnostic power of the ECG is increased exponentially due to the huge number of features that are typically extracted from the ECG signal. Even though this expanded set of features could allow medical staffs to diagnose various pathologies in an accurate way, it is too complex to manage in a manual way and, for this reason, methods for feature representation and evaluation are necessary for supporting medical diagnosis. Starting from this consideration, this paper proposes an enhanced ECG-based decision making system exploiting a collection of ontological models representing the ECG and HRV feature sets and a fuzzy inference engine based on Type-2 Fuzzy Markup Language capable of evaluating the ECG and HRV properties related to a given person and infer detailed information about his health quality level. As will be shown in the experimental section, where the proposed approach has been tested on a set of under exams students, our diagnostic framework yields good performances both in terms of precision and recall.     

Semia: semi-automatic interactive graphic editing tool to annotate ambulatory ECG records

We designed and developed a special purpose interactive graphic editing tool semi-automatic (Semia) to annotate transient ischaemic ST segment episodes and other non-ischaemic ST segment events in 24h ambulatory electrocardiogram (ECG) records. The tool allows representation and viewing of the data, interaction with the data globally and locally at different resolutions, examining data at any point, manual adjustment of heart-beat fiducial points, and manual and automatic editing of annotations. Efficient and fast display of ambulatory ECG signal waveforms, display of diagnostic and morphology feature-vector time-series, dynamic interface controls, and automated procedures to help annotate, made the tool efficient, user friendly and usable. Human expert annotators used the Semia tool to successfully annotate the Long-Term ST database (LTST DB), a result of a multinational effort. The tool supported paperless editing of annotations at dislocated geographical sites. We present design, characteristic "look and feel", functionality, and development of Semia annotating tool.     

Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition

Although the electrocardiogram (ECG) has been a reliable diagnostic tool for decades, its deployment in the context of biometrics is relatively recent. Its robustness to falsification, the evidence it carries about aliveness and its rich feature space has rendered the deployment of ECG based biometrics an interesting prospect. The rich feature space contains fiducial based information such as characteristic peaks which reflect the underlying physiological properties of the heart. The principal goal of this study is to quantitatively evaluate the information content of the fiducial based feature set in terms of their effect on subject and heart beat classification accuracy (ECG data acquired from the PhysioNet ECG repository). To this end, a comprehensive set of fiducial based features was extracted from a collection of ECG records. This feature set was subsequently reduced using a variety of feature extraction/selection methods such as principle component analysis (PCA), linear discriminant analysis (LDA), information-gain ratio (IGR), and rough sets (in conjunction with the PASH algorithm). The performance of the reduced feature set was examined and the results evaluated with respect to the full feature set in terms of the overall classification accuracy and false (acceptance/rejection) ratios (FAR/FRR). The results of this study indicate that the PASH algorithm, deployed within the context of rough sets, reduced the dimensionality of the feature space maximally, while maintaining maximal classification accuracy.     

运动状态下的智能远程心电监测预警系统设计

基于单导联方式设计了一款具有运动状态识别、心电分级预警、远程无线传输等功能于一体的新型心电监测系统.本设计通过基于STM32 F103芯片的心电监测终端,将人体表面采集到的微弱心电信号进行滤波和放大处理,并结合加速度传感器实时获取人体的运动信息.将采集到的心电信号进行分析处理,有针对性地提出建议和警示,达到提前预警及紧急救助的目的.实验结果表明,采集的心电波形具有良好的医学参考价值,可为未来心血管疾病的远程智能医疗提供一定的技术支持.     

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。     

Serial VCG/ECG Analysis Using Neural Networks

Serial ECG analysis is an important diagnostic tool in which two or more successive ECG recordings from the same patient are compared in order to find changes due to, e.g. myocardial infarction. The present study investigates a new approach to serial analysis which is based on artificial neural networks. Interrecording changes are sometimes falsely detected due to electrode misplacement or positional changes of the heart. In order to compensate for such problems, a new technique for VCG loop alignment was employed. A study population of 1000 patients with two recordings was used and manually scrutinized by three experienced ECG interpreters. Pathological changes indicating newly developed infarcts were found in 256 patients. Different combinations of VCG/ECG measurements served as input data to the neural network. The best performance of the neural network was obtained when ECG and VCG measurements were combined and the resulting sensitivity was 69% at a specificity of 90%. The use of only ECG or VCG measurements reduced the sensitivity to 63% and 60%, respectively. The results indicated that serial analysis based on neural networks did not improve significantly when VCG loop alignment was included.     

心电节拍自动分类算法的研究

世界卫生组织调查发现在全球范围内心血管、心脏疾病是导致死亡概率最高的疾病,心电图(ECG)是临床上广泛应用的预防、监护和诊断心血管及心脏疾病的重要工具之一。心电自动分析诊断技术可以大大减少心电医师的工作量,提高心电图的诊断效率,其中心电节拍(ECG Beat)分类是心电自动分析诊断技术的主要研究方向,是自动分析心律失常的一种重要分析手段,特别是在动态心电图或者长期心电记录领域发挥着重要的作用。本文提出一种心电节拍分类算法,该算法在聚类分析的基础上,结合线性分类器加权判断和心电医师对各聚类的抽样判断,获得心电节拍的最终分类。以MIT-BIH-AR[1]心律失常数据库作为原始数据,采用AAMI的ANSI/AAMI EC57:1998/(R)2003[2]标准规定的心电节拍分类种类及准确率的衡量方法,对该算法的检验,发现采用聚类分析和线性分类器加权判断的方法,分类的准确率达到86.60%;结合心电医师的抽样判断后,算法最终的准确率高达98.16%。     

Probabliistic Analysis Of Electrocardiogram (Ecg) Heart Signal

Electrocardiography (ECG) is a heart signal wave that is recorded using medical sensors, which are normally attached to the human body by the heart. ECG waves have repetitive patterns that can be efficiently used in the diagnosis of heart problems as they carry several characteristics of heart operation. Traditionally, the analysis of ECG waves is done using informal techniques, like simulation, which is in-exhaustive and thus the analysis results may lead to ambiguities and life threatening scenarios in extreme cases. In order to overcome such problems, we propose to analyze ECG heart signals using probabilistic model checking, which is a formal methods based quantitative analysis approach. This work presents the formal probabilistic analysis of ECG signal abnormalities where the likelihood of abnormal patterns is studied and analyzed using the PRISM model checker.     

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.     

基于STM32便携式心电监护装置的设计

综合运用传感器技术、嵌入式技术和无线通信技术,设计了一种以STM32F103C8T6微处理器为核心的家用便携式心电监护装置.利用Pulse Sensor脉搏心率传感器采集心电信号,经STM32内置的模数转换器(ADC)处理得到数字信号.LCD屏用于实时显示心率值和心电图(ECG),心率数据通过蓝牙通信模块实时传输到手机...     

窦性心律失常心电序列的可视化研究

对心电信号序列与心血管疾病之间存在关系的探索是研究心脏病临床诊断的一类经典论题。心电图是检测心脏病的重要工具,目前已采集到长期的批量数据,对其进行处理和判别具有实际意义。利用变值心电测量系统,对窦性心律T波改变这一特殊心电数据和正常心电序列进行处理形成2D散点图谱,以可视化的形式展示这两类心电信号的分布特征和异同[1],与传统心电图相比,所提方法具有直观透明易懂的特点;同时也列举了不同测量变量值情况下的心电序列可视化结果。     

WebECG: A novel ECG simulator based on MATLAB Web Figure

Electrocardiogram is a signal containing information about the condition and operation of heart. Nowadays, many heart diseases can be efficiently diagnosed using these signals. However, a proper recognition and classification of the heart signals are essential requirement for the diagnosis of heart diseases. In this study, emphasizing on this requirement, a new ECG simulator based on MATLAB Web Figure called WebECG is designed and implemented to facilitate the education on ECG signals. Advanced flexibility and good visualization capabilities including 3-dimension view, zoom and move on ECG graphics are provided by WebECG. The users are able to plot ECG signals with different parameters, to plot the ECGs of nine arrhythmia types. Furthermore, WebECG is capable to add three different noises to ECG and to plot/analyze long-term ECGs. These properties of the WebECG support efficient web-based education of ECG signals.     

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.     

ECG waveform analysis by significant point extraction. I. Data reduction

We present a new technique for automatic data reduction and pattern recognition of time-domain signals such as electrocardiogram (ECG) waveforms. Data reduction is important because only a few significant features of each heart beat are of interest in pattern analysis, while the patient data collection system acquires an enormous number of data samples. We present a significant point extraction algorithm, based on the analysis of curvature, that identifies data samples that represent clinically significant information in the ECG waveform. Data reduction rates of up to 1:10 are possible without significantly distorting the appearance of the waveform. This method is unique in that common procedures help in both data reduction as well as pattern recognition. Part II of this work deals specifically with pattern analysis of normal and abnormal heart beats.     

A novel approach for phonocardiographic signals processing to make possible fetal heart rate evaluations

In this paper, a non-invasive, portable and inexpensive antenatal care system is developed using fetal phonocardiography. The fPCG technique has the potential to provide low-cost and long-term diagnostics to the under-served population. The fPCG signal contains valuable diagnostic information regarding fetal health during antenatal period. The fPCG signals are acquired from the maternal abdominal surface using a wireless data acquisition and recording system. The diagnostic parameters e.g., baseline, variability, acceleration and deceleration of the fetal heart rate are derived from the fPCG signal. A model based on adaptive neuro-fuzzy inference system is developed for the evaluation of fetal health status. To study the performance of the developed system, experiments were carried out with real fPCG signals under the supervision of medical experts. Its performance is found to be in close proximity with the widely accepted Doppler ultrasound based fetal monitor results. The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio. The system can be used as first screening tool by the medical practitioners.     

Prototyping flexible touch screen devices using collocated haptic-graphic elastic-object deformation on the GPU

Rapid advances in flexible display technologies and the benefits that they provide are promising enough to consider them for futuristic mobile devices. Current prototyping methods lack facilities to simulate such flexible touch screen displays and the interaction with them. In this paper, we present a technique that provides product developers a tool to interactively simulate products featuring flexible displays, using Augmented Reality and Haptics. This GPU-based algorithm is computationally inexpensive and efficient to deform a polygonal mesh in real time while maintaining an acceptable haptic feedback. The implementation of the algorithm has been found to be successful when applied to a variety of product simulations. This simulation tool can enhance or even replace traditional prototyping and facilitate testing of the prototype at various stages of the design cycle.     

可穿戴心电信号采集与分析系统的设计与实现

针对传统心电采集设备的移动限制性以及佩戴的不舒适性,根据可穿戴计算特点,设计并实现了穿戴式心电采集与分析系统。系统采用自主研发的12/单导联心电采集模块进行心电信号采集,数据可存于采集设备或经3G网络传输到服务器端,同时所开发的软件可对心电图进行辅助病情分析,实现对佩戴人的心电监护。还研究并制作了插入式电极和织物电极,并通过二者的结合提高了采集心电信号的质量。实际佩戴和使用结果表明,使用插入式织物电极的可穿戴式心电采集设备具有良好的舒适性,心电信号波形的质量能够达到临床监控的要求。     

Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices

In many health care situations, powerful mobile tools may help to make decisions and provide support for continuous education and training. They can be useful in emergency conditions and for the supervised application of protocols and procedures. To this end, content models and formats with semantic and intelligence have more flexibility to provide medical personnel (both in off-line and on-line conditions) with more powerful tools than those currently on the market. In this paper, we are presenting Mobile Medicine solution, which exploits a collection of semantic computing technologies together with intelligent content model and tools to provide innovative services for medical personnel. Most of the activities of semantic computing are performed on the back office on a cloud computing architecture for: clustering, recommendations, intelligent content production and adaptation. The mobile devices have been endowed with a content organizer to collect local data, provide local suggestions, while supporting taxonomical searches and local queries on PDA and iPhone. The proposed solution is under usage at the main hospital in Florence. The smart content has been produced by medical personnel, with the adoption of the new ADF-Design authoring tool, which produces content in MPEG-21 format. The mobile content distribution service is integrated with a collaborative networking portal, for discussion on procedures and content, thus suggestions are provided on both PC and Mobiles (PDA and iPhone).