分类技术在心电图自动诊断模型中的应用比较

提高心电图诊断的有效性和准确性的关键在于心电图分类的质量。文中针对这一情况，详细论述了利用各种分类技术对提取的心电图特征数据进行分类的方法，并在比较各种分类算法的基础上，提出了一种基于CBR的心电图自动诊断系统的结构模型。     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

Application of InP Neural Network to ECG BeatClassification

This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together.     

基于HVS视觉模型下的图像降噪

描述了构建人类视觉系统(Human vision system，HVS)降噪模型的思想方法及理论依据。为了解决SureShrink及VisuShrink方法对重建图像带来的不良影响，建立了基于HVS模型的适应小波门限降噪模型，提出HVSShrink降噪策略。通过对该算法进行测试和比较，证明了使用本方法可以得到具有较好视觉效果的重建图像。     

数字式遥测心电监护系统的设计和实现

提出的遥测心电监护系统采用蓝牙无线技术监测病人的心电等生理参数,克服了使用传统的有线系统及病人不能自由活动的缺点。系统的发射机主要由蓝牙模块ROK101007和带有8通道12位A/D转换器的微控制器ADuC812组成,ADuC812主要进行心电数据的采集和控制,两者之间通过UART接口进行指令和数据的传输。接收机通过USB接口与PC机或者笔记本电脑实现高速数据通信而不需要外加工作电源。试验表明,该系统能有效地无线传输心电数据。     

信号Lipschitz奇异性的计算与分析

该文从Lipschitz奇异性的定义出发,根据信号与噪声的奇异性指数α的不同,推导出了小波变换系数点是噪声点还是信号点的判别法,也为计算Lipschitz指数提供了一个简单可靠的方法。仿真结果证明,该方法对于信号去噪能起到良好的效果。     

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.     

Cloud enabled fractal based ECG compression in wireless body sensor networks

E-health applications deal with a huge amount of biological signals such as ECG generated by body sensor networks (BSN). Moreover, many healthcare organizations require access to these records. Therefore, cloud is widely used in healthcare systems to serve as a central service repository. To minimize the traffic going to and coming from cloud ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal based ECG lossy compression technique is proposed. It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios. The proposed technique is based on modifying the popular fractal model to be used in compression in conjunction with the iterated function system. The ECG signal is divided into equal blocks called range blocks. Subsequently, another down-sampled copy of the ECG signal is created which is called domain. For each range block the most similar block in the domain is found. As a result, fractal coefficients (i.e. parameters defining fractal compression model) are calculated and stored inside the compressed file for each ECG signal range block. In order to make our technique cloud friendly, the decompression operation is designed in such a way that allows the user to retrieve part of the file (i.e. ECG segment) without decompressing the whole file. Therefore, the clients do not need to download the full compressed file before they can view the result. The proposed algorithm has been implemented and compared with other existing lossy ECG compression techniques. It is found that the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.     

Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition

This paper proposes a novel scheme of feature selection, which employs a modified genetic algorithm that uses a variable-range searching strategy and empirical mode decomposition (EMD). Combined with support vector machines (SVMs), a new pattern recognition method for electrocardiograph (ECG) is developed. First, the ECG signal is decomposed into intrinsic mode functions (IMFs) that represent signal characteristics with sample oscillatory modes. Then, the modified genetic algorithm with variable-range encoding and dynamic searching strategy is used to optimize statistical feature subsets. Next, a statistical model based on receiver operating characteristic (ROC) analysis is developed to select the dominant features. Finally, the SVM-based pattern recognition model is used to classify different ECG patterns. Comparative studies with peer-reviewed results and two other well-known feature selection methods demonstrate that the proposed method can select dominant features in processing ECG signal, and achieve better classification performance with lower feature dimensionality.     

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.