ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.     

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.     

纺织结构柔性心电电极研究进展

心电电极是心电信号监测的关键器件,性能良好的心电电极对于准确获取心电信号至关重要,从而可实现对心血管疾病的实时监控和有效预防。纺织结构柔性心电电极是可穿戴心电电极的一种新趋势,为可穿戴心电监护提供了可能。从心电电极的材料、织物结构、尺寸、形状、整体结构几个方面对纺织结构柔性心电电极进行介绍,并列举了若干国内外研究实例。最后指出纺织结构柔性心电电极设计与研发需要解决的问题,进一步对纺织结构柔性心电电极的发展进行展望。     

一种适用于心电信号检测的高阶连续时间OTA-C滤波器设计

提出一种适合心电信号（ECG）检测的OTA-C滤波器。为了达到低功耗、低截止频率、高直流增益、高阻带衰减、低谐波失真的目的,滤波器采用五阶巴特沃斯全差分低通滤波结构和高增益的两级单端输出OTA,其中OTA电路采用亚阈值区驱动、电流分流和源极负反馈等技术。采用SMIC 0.18-μm 1P6M CMOS工艺进行电路、版图设计及优化。仿真结果表明,滤波器在静态功耗为17.6 μW,截止频率为240 Hz,直流增益为-6 dB,阻带衰减为120 dB每十倍频,三次谐波失真小于-62 dB@ 400 mV,适合应用于心电信号检测模拟前端。     

一种超宽线性范围低通带衰减的OTA-C心电信号滤波器设计

介绍了一种基于全差分运算跨导放大器（OTA）的超宽线性范围低通带衰减的五阶Butterworth低通滤波器。该滤波器主要应用于可穿戴式无线体域网的UWB健康监护与遥测系统。为了提高OTA-C滤波器线性范围,对典型小跨导电路的源极负反馈结构进行了改进,并将共源共栅结构作为OTA的输出级以减少滤波器的通带衰减。为了适应生物医学芯片的低功耗特性,基于OTA结构的电路工作在亚阈值区。电路基于SMIC 0.18-μm CMOS工艺进行设计并流片。测试结果表明,滤波器的通带衰减仅为6.2dB,-3-dB频率为276 Hz;对于输入100 Hz、0.8 VPP的正弦信号,该滤波器的总谐波失真（THD）为56.8 dB。利用该滤波器对含有噪声干扰的ECG信号进行滤波, 结果证明了该滤波器能有效地滤除噪声干扰。     

一种基于指令域示波器的数控加工工艺参数优化方法

针对当前国内外数控加工工艺参数优化只能离线操作的问题,进行了基于华中数控8型高档数控系统二次开发,完成了一套实时采集的指令域示波器模块,实现机床数据挖掘、分析、工艺参数优化等功能与数控系统的集成,完成机床加工状态心电图实时监测。通过指令域分析加工负载电流优化进给速率,可以大大提高加工效率。该方法操作方便,已经在航空航天复杂零件加工等领域得到了验证应用,加工效率提高了30%~40%。     

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.     

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%.