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.     

一种实时心电图数据压缩算法

提出了一种实时心电图ＥＣＧ数据压缩算法。它是将自适应变门限算法与转折点算法相结合。自适应变门限算法是对ＡＺＴＥＣ算法的改进．它计算ＥＣＧ信号的几个统计参数来确定可变门限值。转折点算法是分析采样点的趋势并只存储每对连续的采样点中的一个。它保留信号的斜坡标志发生变化的峰点和谷点。本文算法兼有这两种算法的优点。这种算法在较高压缩比的情况下重建心电图信号失真较小．     

心电图信号的频域分析

提出了一种简便有效的、由心电图（ECG）曲线提取QRS-T复合波的新方法，从而获得P波（当房颤未发生时）或f波（当房颤时）；利用功率谱、自相关、互相关等信号处理技术，对它进行了处理和分析，特别是，比较了未房颤时和发生房颤时的相关信息的显著差别。     

心电图数据压缩技术

本文介绍了在计算机上实现的二种心电图数据压缩方法，并详述了每种压缩法对心电图压缩技术的压缩比、失真度的影响。     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

数千上万核上模拟结果的并行可视化

在科学与工程计算中,在数千上万核上,模拟包含上亿网格单元的模型通常输出TB量级的时变数据集。这些数据集包含多个时刻的数据,每个时刻的数据分布存储在数千个文件,单时刻的数据量达到几GB甚至几十GB。为了并行地可视化这种时变数据集,本文设计了基于网格片的层次化数据结构,基于该数据结构改进和优化了并行可视化流程和数据通信算法。在集成到可视化软件后,实现了两个应用的模拟结果可视化。实测数据表明,对于单时刻5GB和32GB的数据,在数十上百个处理器核上,从数据读入到获得体绘制结果的时间分别为19秒和80秒,交互操作为2秒到10秒;三维面绘制在数秒钟内,切片分析在2秒以内。     

Raw data extraction from electrocardiograms with Portable Document Format

During the last two decades there has been a thorough research and development of standards and protocols in order to cope with different electrocardiogram formats from heterogeneous acquisition systems. Despite the efforts of public and private consortiums on creating a standardized electrocardiogram (ECG) storage format, there is still not a single one. Indeed, there is also the necessity of access to raw data of the ECGs previously acquired. Most of these documents have been saved as Adobe PDF files, since for medical staff it is an easy format for later visualization. However, this format presents difficulties when trying to access original raw data for subsequent studies and signal analysis. In this manner, this paper presents an application that obtains plain numerical data from ECG files stored with PDF format. Data can also be exported to one of the most common file formats in existence, to be easily accessed thereafter.     

Event-driven,pattern-based methodology for cost-effective development of standardized personal health devices

Experiences applying standards in personal health devices (PHDs) show an inherent trade-off between interoperability and costs (in terms of processing load and development time). Therefore, reducing hardware and software costs as well as time-to-market is crucial for standards adoption. The ISO/IEEE11073 PHD family of standards (also referred to as X73PHD) provides interoperable communication between PHDs and aggregators. Nevertheless, the responsibility of achieving inexpensive implementations of X73PHD in limited resource microcontrollers falls directly on the developer. Hence, the authors previously presented a methodology based on patterns to implement X73-compliant PHDs into devices with low-voltage low-power constraints. That version was based on multitasking, which required additional features and resources. This paper therefore presents an event-driven evolution of the patterns-based methodology for cost-effective development of standardized PHDs. The results of comparing between the two versions showed that the mean values of decrease in memory consumption and cycles of latency are 11.59% and 45.95%, respectively. In addition, several enhancements in terms of cost-effectiveness and development time can be derived from the new version of the methodology. Therefore, the new approach could help in producing cost-effective X73-compliant PHDs, which in turn could foster the adoption of standards.     

A usability evaluation of medical software at an expert conference setting

Introduction

A usability test was employed to evaluate two medical software applications at an expert conference setting. One software application is a medical diagnostic tool (electrocardiogram [ECG] viewer) and the other is a medical research tool (electrode misplacement simulator [EMS]). These novel applications have yet to be adopted by the healthcare domain, thus, (1) we wanted to determine the potential user acceptance of these applications and (2) we wanted to determine the feasibility of evaluating medical diagnostic and medical research software at a conference setting as opposed to the conventional laboratory setting.

Methods

The medical diagnostic tool (ECG viewer) was evaluated using seven delegates and the medical research tool (EMS) was evaluated using 17 delegates that were recruited at the 2010 International Conference on Computing in Cardiology. Each delegate/participant was required to use the software and undertake a set of predefined tasks during the session breaks at the conference. User interactions with the software were recorded using screen-recording software. The ‘think-aloud’ protocol was also used to elicit verbal feedback from the participants whilst they attempted the pre-defined tasks. Before and after each session, participants completed a pre-test and a post-test questionnaire respectively.

Results

The average duration of a usability session at the conference was 34.69 min (SD = 10.28). However, taking into account that 10 min was dedicated to the pre-test and post-test questionnaires, the average time dedication to user interaction of the medical software was 24.69 min (SD = 10.28). Given we have shown that usability data can be collected at conferences, this paper details the advantages of conference-based usability studies over the laboratory-based approach. For example, given delegates gather at one geographical location, a conference-based usability evaluation facilitates recruitment of a convenient sample of international subject experts. This would otherwise be very expensive to arrange. A conference-based approach also allows for data to be collected over a few days as opposed to months by avoiding administration duties normally involved in laboratory based approach, e.g. mailing invitation letters as part of a recruitment campaign.Following analysis of the user video recordings, 41 (previously unknown) use errors were identified in the advanced ECG viewer and 29 were identified in the EMS application. All use errors were given a consensus severity rating from two independent usability experts. Out of a rating scale of 4 (where 1 = cosmetic and 4 = critical), the average severity rating for the ECG viewer was 2.24 (SD = 1.09) and the average severity rating for the EMS application was 2.34 (SD = 0.97). We were also able to extract task completion rates and times from the video recordings to determine the effectiveness of the software applications. For example, six out of seven tasks were completed by all participants when using both applications. This statistic alone suggests both applications already have a high degree of usability. As well as extracting data from the video recordings, we were also able to extract data from the questionnaires. Using a semantic differential scale (where 1 = poor and 5 = excellent), delegates highly rated the ‘responsiveness’, ‘usefulness’, ‘learnability’ and the ‘look and feel’ of both applications.

Conclusion

This study has shown the potential user acceptance and user-friendliness of the novel EMS and the ECG viewer applications within the healthcare domain. It has also shown that both medical diagnostic software and medical research software can be evaluated for their usability at an expert conference setting. The primary advantage of a conference-based usability evaluation over a laboratory-based evaluation is the high concentration of experts at one location, which is convenient, less time consuming and less expensive.     

基于改进遗传模拟退火 K-means的心电波形的分类研究

针对心电图自动诊断困难这一问题,提出了一种新的聚类算法：基于均方差属性加权的遗传模拟退火K-means改进聚类算法,用于改进心电图（ECG）信号的自动识别技术。利用小波变换的多分辨率和抗干扰能力好的特点,检测QRS波、P波、T波,提高了特征检测的准确性;利用聚类分析具有较好的鲁棒性和适合于大数据量分析的特点,对心电信号进行波形分类。采用MIT-BIH标准心电数据库中的部分数据对识别结果进行判断,改进后的K-means聚类算法的准确率高于传统的K-means聚类算法,实验表明该算法对心电信号可以进行有效分类。