An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.     

ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix

We propose a systematic ECG quality classification method based on a kernel support vector machine（KSVM） and genetic algorithm（GA） to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function（GRBF） is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function（MHWF）. GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search（GS） method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive（TP）, false positive（FP）, and classification accuracy were used as the assessment indices. For training database set A（1000 recordings）, the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were： TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B（500 recordings）, the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.     

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。     

Automatic pattern recognition in ECG time series

In this paper, a technique for the automatic detection of any recurrent pattern in ECG time series is introduced. The wavelet transform is used to obtain a multiresolution representation of some example patterns for signal structure extraction. Neural Networks are trained with the wavelet transformed templates providing an efficient detector even for temporally varying patterns within the complete time series. The method is also robust against offsets and stable for signal to noise ratios larger than one. Its reliability was tested on 60 Holter ECG recordings of patients at the Department of Cardiology (University of Bonn). Due to the convincing results and its fast implementation the method can easily be used in clinical medicine. In particular, it solves the problem of automatic P wave detection in Holter ECG recordings.     

基于电容耦合的多导联心电监测系统研究

传统的非接触式心电监测系统在硬质印刷电路板构建的电容电极基础上,使用单导联方式进行心电监测,且仅根据心率变化进行心脏异常诊断,无法满足当前临床诊断标准.基于电容耦合原理,设计了一款多导联心电监测系统,将3个由导电织布构成的柔性电容电极集成于椅座背部,用于获取标准肢体导联Ⅰ、Ⅱ、Ⅲ和加压单极肢体导联 aVR、aVL、aVF等6导联心电信号.借由导电织布式柔性电容电极与人体表面较好的接触效应,可以有效减小运动伪影的产生.通过与硬质PCB电极输出的心电波形相比,使用本系统的患者在移动情况下输出的心电波形保持相对平稳状态,无明显运动伪影.系统实验证实了此新型多导联心电监测仪的有效性,不仅可以满足个人日常心电监测使用,而且适于长期动态心电监测.     

Multilead ECG analysis

This paper describes the results of our recent research in computer-assisted ECG/VCG interpretation. It comprises new developments which were initiated by the advent of relatively inexpensive microcomputers. Our previous systems performed an off-line analysis of ECGs. Currently, there is a trend to move computer power near to the patient and to provide on-line analysis of ECGs. Besides the advantage of the direct availability of the ECG interpretation, quality control will reduce the number of uninterpretable ECGs and hence the number of repeated recordings. This paper describes the requirements that were established for a system for on-line ECG analysis. The system is based on our modular approach, just like our off-line system, Modular ECG ANalysis System (MEANS). Changes in the methods and software had to be made mainly because of the simultaneity of all ECG leads and the concurrency of the processing tasks. Other modifications and extensions of the algorithms necessary to meet the requirements of on-line ECG interpretation especially those related to processing speed, are discussed, and evaluation results are presented.     

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.     

Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models

This study presented a new diagnosis system for myocardial infarction classification by converting multi-lead ECG data into a density model for increasing accuracy and flexibility of diseases detection. In contrast to the traditional approaches, a hybrid system with HMMs and GMMs was employed for data classification. A hybrid approach using multi-leads, i.e., lead-V1, V2, V3 and V4 for myocardial infarction were developed and HMMs were used not only to find the ECG segmentations but also to calculate the log-likelihood value which was treated as statistical feature data of each heartbeat's ECG complex. The 4-dimension feature vector extracted by HMMs was clustered by GMMs with different numbers of distribution (disease and normal data). SVMs classifier was also examined for comparison with our system in experimental result. There were total 1129 samples of heartbeats from clinical data, including 582 data with myocardial infarction and 547 normal data. The sensitivity of this diagnosis system achieved 85.71%, specificity achieved 79.82% and accuracy achieved 82.50% statistically.     

An improved method for on-line averaging and detecting of ECG waveforms

The most widely used methods for accurate signal averaging were studied and compared in order to gain a better understanding of the qualities and performances of each method. The level-triggering, contour-limiting, and correlation methods were simulated and compared. A new correlation method which is a weighted correlation of differences proved to be most suitable for real-time signal averaging, and detection of waveforms' variations. Simulated ECG waveforms and real ECG recordings were analyzed in this study. Twenty-eight ECG recordings of unipolar leads for noninvasive detection of the His-Purkinje activity were averaged separately by each method. The success in detection and the signal to noise ratio of the detected His activity obtained by each method was compared. Simulated ECG waveforms with random noise added were analyzed by four methods and the correct alignment as a function of the noise level was measured. The performance of our method in rejection of noisy waveforms and in detection of small variations in the waveforms is demonstrated.     

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.     

基于智能终端的心电诊疗与远程医疗系统

利用Socket通信方式、SQL Server DBMS以及自主开发的3．5mm通信协议。设计并实现基于智能终端的心电诊疗与远程医疗系统。该系统包括一套远程医疗软件和心电信号调理设备。该系统使患者可以在家中随时利用智能终端将心电数据实时的传输给医疗工作者,将心血管病的实时诊疗从医院转移到家中。减轻医院工作压力的同时也满足患者对医疗监护的需求。     

基于物联网的社区心电监护系统设计

将物联网与传统心电图（ ECG）监护系统相结合,以Zig Bee无线通信技术为核心,设计了一种具有自动报警功能的社区心电监护系统,实现了心电数据的自动采集、处理、诊断、异常报警与无线传输。该系统采用无线通信技术传输数据,可以减少系统的连线。系统中的心电分析算法可以实时显示和在线分析心电信号,提取心电信号中的疾病特征,实现心脏疾病的自动诊断和预测。实验结果表明：所设计的心电监护系统,能够准确采集心电信号。将物联网技术应用在智能心电监护系统上,便于对社区患者进行统一监管。     

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。     

EMGdi signal enhancement based on ICA decomposition and wavelet transform

Diaphragmatic electromyogram (EMGdi) signal plays an important role in the diagnosis and analysis of respiratory diseases. However, EMGdi recordings are often contaminated by electrocardiographic (ECG) interference, which posing serious obstacle to traditional denoising approaches due to overlapped spectra of these signals. In this paper, a novel method based on wavelet transform and independent component analysis (ICA) is proposed to remove the ECG interference from noisy EMGdi signals. With the proposed method, the original independent components of contaminated EMGdi signal were first obtained with ICA. Then the ECG components contained were removed by a specially designed wavelet domain filter. After that, the purified independent components were reconstructed back to the original signal space by ICA to obtain clean EMGdi signals. Experimental results achieved on practical clinical data show that the proposed approach is better than several traditional methods include wavelet transform (WT), ICA, digital filter and adaptive filter in ECG interference removing.     

基于FPGA的ECG信号采集与处理系统设计

当今平板电脑和大屏幕智能手机的普及为便携式心电监护仪的发展带来了机遇。针对这种发展趋势,设计了便携式心电（electrocardiogram,ECG）信号的实时采集、处理和传送系统。该系统以FPGA为控制中心,用高精度24位心电专用芯片实现模数转换,用FIR滤波算法实现高阶数字滤波。该系统采集和处理的ECG数据可传送至平板电脑或大屏幕智能手机,以方便心脏疾病者自我初步诊断。     

一种心电图微型计算机自动诊断系统--CSS1型

本文介绍一个微型计算机(64K,8位)心电图自动诊断系统及其设计思想.该系统体积小(如台式仪器),操作简便,处理一份心电图平均仅需时75秒.适用于心脏病普查和门诊.     

Telemedical support using real-time ultrasonography and endoscopy images

We developed a telecommunications apparatus (TMS-6101, NIHON KOHDEN WELLNESS CORPORATION, Tokyo, Japan) and evaluated its clinical utility as a telemedical support system. It is capable of transmitting on a real-time basis such vital signs as blood pressure, arterial O2 saturation and ECG recordings, which are measured at bedside using the Life-Mate monitor (NIHON KOHDEN WELLNESS Co.). It is also capable of transmitting moving video-camera pictures in real time. In this study we assessed its application for telemedical supports with particular emphasis on ultrasonography and endoscopy images since reports of such applications are lacking. Employing this system, several kinds of technically demanding endoscopic procedures were supported successfully under the supervision of at least one off-site specialist physician. The system proved to be a very useful medical resource, since it facilitated high quality medical care and specialist consultation at any location without those specialists traveling to the scene.     

A clustering based system for instant detection of cardiac abnormalities from compressed ECG

Compressed Electrocardiography (ECG) is being used in modern telecardiology applications for faster and efficient transmission. However, existing ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be applied. This additional process of decompression before performing diagnosis for every ECG packet introduces undesirable delays, which can have severe impact on the longevity of the patient. In this paper, we first used an attribute selection method that selects only a few features from the compressed ECG. Then we used Expected Maximization (EM) clustering technique to create normal and abnormal ECG clusters. Twenty different segments (13 normal and 7 abnormal) of compressed ECG from a MIT-BIH subject were tested with 100% success using our model. Apart from automatic clustering of normal and abnormal compressed ECG segments, this paper presents an algorithm to identify initiation of abnormality. Therefore, emergency personnel can be contacted for rescue mission, within the earliest possible time. This innovative technique based on data mining of compressed ECGs attributes, enables faster identification of cardiac abnormalities resulting in an efficient telecardiology diagnosis system.     

绝缘层参数对非接触电极心电检测性能的影响研究

心电图广泛用于人体心脏电活动特性研究和心脏相关疾病的诊断。常规的接触式湿电极普遍 使用导电膏,容易造成受试者的负担感和不适感,且存在皮肤过敏的风险,也不利于心电信号的长期监测。针对这个问题,该文设计了一种通过皮肤与电极感应层之间的容性耦合来获取心电信号的非接触电极,并搭建了基于 ADS1299 的生理信号采集系统,可实现无需导电膏、无需与皮肤直接接触的心电测量。在此基础上,该文全面研究了介于非接触电极和皮肤之间的绝缘层材料及厚度对心电信号的影响。研究结果表明,非接触电极可获取高质量的心电信号,且绝缘层参数对心电信号质量具有显 著的影响：棉布材料作为绝缘层时,心电信号质量最好;绝缘层厚度越小,心电信号质量越好。该研究结果可为非接触电极在移动健康监护中的进一步广泛应用提供重要的实验基础和理论依据。     

基于深度学习和模糊C均值的心电信号分类方法

针对长时海量心电信号自动分类系统中,心电专家诊断费时、费力和成本高,心电信号形态复杂导致特征提取困难,异常诊断模型适应性差、准确度低等问题,本文提出一种基于深度学习和模糊C均值的心电信号分类方法.该方法主要包括心电信号降噪预处理、心电信号分段和采样点统一化、无监督心跳特征学习、模糊C均值分类4个步骤,给出了模糊C均值深度信念网络FCMDBN模型结构和学习分类算法.仿真实验基于MIT-BIH心率异常数据库表明,与基于传统心电特征人工设计的分类方法相比,本文提出的信号诊断方法具有较高的适应性和准确度.