基于小波变换和矢量量化的二维ECG数据压缩算法

本研究针对心电数据的压缩问题,提出了一种新的基于小波变换的二维心电（ECG）数据压缩算法。该算法首先将一维原始ECG信号转化为二维序列信号,从而使ECG数据的两种相关性可得到充分地利用;然后对二维ECG序列进行小波变换,并对变换后的系数应用了一种改进的矢量量化（VQ）方法。在改进的VQ方法中,根据小波变换后系数的特点,构造了一种新的树矢量（TV）。利用本算法与已有基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明：本算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比,具有一定的应用价值。     

一种新的动态心电数据的神经网络压缩方法

本文提出了一种新的、性能更加稳定的动态心电数据的神经网络压缩算法。该方法采用一种不全联接的三层前馈神经网络,将一个ＥＣＧ心搏表示为三个主要的波即Ｐ波,ＱＲＳ波和Ｔ波。三个波的输入与输出只通过少量的隐层单元相联接,并通过各波的隐层单元将相邻波的边缘联系起来。这种方法的优点是在不增加计算量的情况下提高算法对波形的重现能力、较为有效地避免因为Ｐ波和Ｔ波受到干扰,波形变异或其它因素的影响而导致的波形重现失败,提高神经网络压缩方法的鲁棒性和实用性。     

利用DCT分量差值压缩ECG数据的方法

由于心脏活动的有序性和各心电活动周期波形的相似性 ,各心电活动周期波形的DCT(离散时间余弦变换 )分量也具有一定的相似性。根据这一特点 ,本文提出了在首先使用DCT压缩心电图 (ECG)数据的基础上 ,进一步利用各ECG周期的DCT分量的差值压缩数据的方法     

利用DCT分量差值压缩ECG数据的方法

由于心脏活动的有序性和各心电活动周期波形的相似性,各心电活动周期波形的DCT（离散时间余弦变换）分量也具有一定的相似性。根据这一特点,本文提出了在首先使用DCT压缩心电图（ECG）数据的基础上,进一步利用各ECG周期的DCT分量的差值来压缩数据的方法。     

基于小波网络的动态心电数据压缩算法

本文研究了动态心电信号的非平稳过程特性和动态心电图 (ECG)的诊断信息依据 ,从时间序列建模角度研究数据表示模型和压缩算法 ,采用小波网络 (WN)作为数据表示模型 ,提出了动态心电数据的小波网络压缩算法。本算法对原始心电数据实时地分帧 ,将每帧数据映射为小波网络的网络参数作为原始数据的重构信息。文中详细叙述了小波网络的数据表示原理和分帧压缩算法 ,给出了动态心电数据的压缩 /重建的实验结果并进行分析讨论     

基于DCT系数的二级矢量量化压缩ECG数据的方法

本研究提出了一种新的心电信号压缩方法，该方法对心电数据进行离散余弦变换（DCT）并对DCT变换的结果进行二级矢量量化。该方法不但继承了矢量量化高压缩比的特点，而且在很大程度上降低了矢量量化所需的码书长度进而也降低了码字搜索的运算复杂度。实验证明，该算法是一种有效可行的心电信号压缩方法。     

ECG信号小波变换与峰谷检测算法的研究

本文在ECG信号检测过程中，将ECG信号在3尺度上的Haar小波分解的细节信号模极大值对检测与数学形态学峰谷检测相结合，提出了ECG信号小波变换与峰谷检测算法，该算法弥补了小波变换算法对ECG信号时域特征检测的不足，有效地提高了ECG信号检测的准确度。     

一种基于R-R间期的改进型模板匹配法在心电图自动诊断中的应用

目的探讨R-R间期的改进型模板匹配法在心电图(ECG)自动诊断中的应用。方法选择26例受试者ECG数据作为研究对象,分为3组。10例心律失常患者数据作为实验组,其中男性7例,女性3例,年龄24~75岁,平均年龄58.4岁。再选取10例正常人数据作为对照组,其中男性3例,女性7例,年龄26~45岁,平均年龄32.2岁。另选取6例ECG时间大于6 h的长时间病例数据用以证实算法的稳定性,其中男性3例,女性3例,年龄42~63岁,平均年龄56.6岁。使用改进型的信号模板匹配算法检测R波并进而利用计算机程序计算出诊断心率变异性(HRV)的多个因素,借用美国麻省理工学院生理数据库(MIT/BIH)中数据进行测试,对测试结果分组进行统计学检验。结果所选数据组间R-R间期标准差(SDNN)、相邻R-R间期差值的均方根值(RNSSD)、正常窦性心搏间期的平均值(NNVGR)、相邻R-R间期差值大于50 ms的个数占所有R-R间期个数的百分比(PNN50)的计算结果在实验组与对照组之间差异存在统计学意义(P<0.05)。根据改进型模板匹配法得到的6例长时间病例心律失常患者均值都高于对照组。结论该方法可以用来初步筛选ECG心律失常患者,可作为心电预警自动诊断的一种检测参考方法。     

基于深度残差卷积神经网络的心电信号心律不齐识别

心电图(ECG)信号在采集过程中容易受内部和外部噪声干扰,而且不同患者的ECG信号形态特征差异较大,即使同一患者在不同时间和环境下其ECG信号也会有差异,因此ECG信号特征检测与识别在心脏病远程实时监测与智能诊断中具有一定难度。基于此,本研究提出将小波自适应阈值去噪和深度残差卷积神经网络算法用于多种心律不齐的信号识别过程中。其中,使用小波自适应阈值技术完成ECG信号滤波,并设计了包含多个残差块(residual block)结构的20层卷积神经网络(CNN),即深度残差卷积神经网络(DR-CNN),对5大类心律不齐ECG信号进行了识别。然后,本文采用残差块局部神经网络结构单元构建DR-CNN,缓解了深层网络的收敛难、调优难等问题,克服了CNN随着网络层数增加而导致的退化问题;进一步引入批标准化(batch normalization)技术,保证了网络的平滑收敛。按照美国医疗器械促进协会(AAMI)的心搏分类标准,使用麻省理工学院和波士顿贝丝以色列医院(MIT-BIH)心律不齐数据库中94 091个ECG心搏信号(2个导联),完成了心律不齐多分类、室性异位搏动(Veb)和室上性异位搏动(Sveb)等分类识别实验。实验结果表明,本文所提出的方法在ECG信号多分类、Veb和Sveb识别中的准确率分别达到了99.034 9%、99.498 0%和99.334 7%。在相同的数据集和实验平台下,DR-CNN在分类准确率、特异性和灵敏度上均优于相同结构复杂度的CNN、深度多层感知机等传统算法。DR-CNN算法提高了心律不齐智能诊断的精度,该方法与可穿戴设备、物联网和无线通信技术相结合,可以将心脏病的预防、监测和诊断延伸到家庭、养老院等院外场景,从而提高心脏病患者的救治率,并且有效地节约医疗资源。     

一种可控重构质量的心电信号压缩方法

本文提出了一种基于小波包变换和自适应量化的ECG压缩方法.该方法采用编码率-失真度指标(R-D指标)作为代价函数选择最佳小波基,实现给定重构误差条件下的心电信号有效压缩.     

An enhanced method for real-time modelling of cardiac related biosignals using Gaussian mixtures

Cardiac related biosignals modelling is very important for detecting, classification, compression and transmission of such health-related signals. This paper introduces a new, fast and accurate method for modelling the cardiac related biosignals (ECG and PPG) based on a mixture of Gaussian waves. For any signal, at first, the start and end of the ECG beat or PPG pulse is detected, then the baseline is detected then subtracted from the original signal, after that the signal is divided into two signals positive and negative, each modelled separately then incorporated together to form the modelled signal. The proposed method is applied in the MIMIC, and MIT-BIH Arrhythmia databases available online at PhysioNet.     

Electrocardiogram beat detection enhancement using Independent Component Analysis

Beat detection is a basic and fundamental step in electrocardiogram (ECG) processing. In many ECG applications strong artifacts from biological or technical sources could appear and cause distortion of ECG signals. Beat detection algorithm desired property is to avoid these distortions and detect beats in any situation. Our developed method is an extension of Christov's beat detection algorithm, which detects beat using combined adaptive threshold on transformed ECG signal (complex lead). Our offline extension adds estimation of independent components of measured signal into the transformation of ECG creating a signal called complex component, which enhances ECG activity and enables beat detection in presence of strong noises. This makes the beat detection algorithm much more robust in cases of unpredictable noise appearances, typical for holter ECGs and telemedicine applications of ECG. We compared our algorithm with the performance of our implementation of the Christov's and Hamilton's beat detection algorithm.     

基于小波变换的心电信号准无损压缩算法

提出了基于小波变换的心电信号准无损压缩算法。在对原始信号进行一级小波分解的基础上，根据高频分量和低频分量所占位数的不同分别进行无损压缩。实验结果表明该方法失真度非常小，而且算法简单，运算速度快。     

On lossy transform compression of ECG signals with reference to deformation of their parameter values

Electrocardiogram (ECG) signals are the most prominent biomedical signal type used in clinical medicine. Their compression is important and widely researched in the medical informatics community. In the previous literature compression efficacy has been investigated only in the context of how much known or developed methods reduced the storage required by compressed forms of original ECG signals. Sometimes statistical signal evaluations based on, for example, root mean square error were studied. In previous research we developed a refined method for signal compression and tested it jointly with several known techniques for other biomedical signals. Our method of so-called successive approximation quantization used with wavelets was one of the most successful in those tests. In this paper, we studied to what extent these lossy compression methods altered values of medical parameters (medical information) computed from signals. Since the methods are lossy, some information is lost due to the compression when a high enough compression ratio is reached. We found that ECG signals sampled at 400 Hz could be compressed to one fourth of their original storage space, but the values of their medical parameters changed less than 5% due to compression, which indicates reliable results.     

An ischemia detection method based on artificial neural networks

An automated technique was developed for the detection of ischemic episodes in long duration electrocardiographic (ECG) recordings that employs an artificial neural network. In order to train the network for beat classification, a cardiac beat dataset was constructed based on recordings from the European Society of Cardiology (ESC) ST-T database. The network was trained using a Bayesian regularisation method. The raw ECG signal containing the ST segment and the T wave of each beat were the inputs to the beat classification system and the output was the classification of the beat. The input to the network was produced through a principal component analysis (PCA) to achieve dimensionality reduction. The network performance in beat classification was tested on the cardiac beat database providing 90% sensitivity (Se) and 90% specificity (Sp). The neural beat classifier is integrated in a four-stage procedure for ischemic episode detection. The whole system was evaluated on the ESC ST-T database. When aggregate gross statistics was used the Se was 90% and the positive predictive accuracy (PPA) 89%. When aggregate average statistics was used the Se became 86% and the PPA 87%. These results are better than other reported.     

基于功率谱特征的心电数据小波压缩方法

利用心电功率谱特征,探索心电数据压缩新方法。用小波分解心电信号为高频与低频分量,对低频分量继续分解达到要求的级数,对高频分量则根据其所在频段的能量,对临床诊断的价值加以取舍。对MIT生理信号数据库心电数据的压缩与还原分析表明,该方法平衡了压缩比与还原精度之间的矛盾,既具有较高的压缩比,又具有较高的还原精度,而且对信号的适应性也明显增强。另外,该压缩方法还具有一定的去噪作用。说明结合心电功率谱特征与小波变换方法压缩心电有其优势。     

Compression of ECG signals by optimized quantization of discrete cosine transform coefficients.

This paper presents an ECG compressor based on optimized quantization of Discrete Cosine Transform (DCT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each DCT block is quantized using a quantization vector and a threshold vector that are specifically defined for each signal. These vectors are defined, via Lagrange multipliers, so that the estimated entropy is minimized for a given distortion in the reconstructed signal. The optimization method presented in this paper is an adaptation for ECG of a technique previously used for image compression. In the last step of the compressor here proposed, the quantized coefficients are coded by an arithmetic coder. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. To assess the performance of the proposed compressor, 2-minute sections of all 96 records of the MIT-BIH Arrhythmia Database were compressed at different PRD values, and the corresponding compression ratios were computed. We also present traces of test signals before and after the compression/decompression process. The results show that the proposed method achieves good compression ratios (CR) with excellent reconstruction quality. An average CR of 9.3:1 is achieved for PRD equal to 2.5%. Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors.     

基于整数小波变换和嵌入式编码的ECG信号压缩

针对一般的有损压缩方法不能同时实现无损压缩的情况,实现了一类基于整数小波变换和嵌入式编码的心电数据压缩方法,不仅能够进行有损压缩,也能够实现无损压缩.并且比较了EZW、SPIHT和SPECK三种主要的嵌入式编码算法的性能优劣,为移动心电监护以实时性、信号质量和编码可伸缩性为根据选择数据压缩方法提供了参考.