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.     

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.     

平移不变小波在心音信号去噪中的应用

小波阈值去噪方法可以消除心音信号中的噪声,但其缺乏平移不变性,可能在信号的奇异点附近产生人为的振荡现象,即Pesudo-Gibbs现象,影响去噪效果。采用平移不变(Translation Invariance,TI)小波阈值去噪的方法对心音信号进行去噪,通过对信号序列平移来改变奇异点在整段信号的位置,以降低或消除振荡。对信号采用平移不变小波去噪之前,先通过消除趋势项来降低信号采集过程中引入的干扰。实验结果表明,该方法消除了人为振荡现象,在保留心音信号主要特征的前提下,信号的信噪比(Signal-to-Noise Ratio,SNR)和根均方误差(Root Mean Square Error,RMSE)均得到明显改善。     

Semisupervised Sparse Multilinear Discriminant Analysis

Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.     

Super-Resolution image Reconstruction Based on Iteration and Wavelet Transform

In this paper, we provide a super-resolution image reconstruction algorithm based on wavelet transform. Wavelet transform can separate high frequency and low frequency information of image. The more high frequency information can be obtained by using wavelet transform and the technique of image fusion. Meanwhile, reconstructed super-resolution image is produced by the iterative method. In iteration process, noise of image can be suppressed by applying method of wavelet threshold de-noising. The experiment results show that the algorithm can overcome the disadvantage of the classical interpolation method and effectively improve the resolution and PSNR of the image.     

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。     

图像小波阈值去噪方法研究

该文介绍了小波阈值去噪的基本原理,描述了图像小波阈值法去噪的过程,并通过仿真实验结果比较了小波阈值去噪方法在选取不同阈值函数下的去噪效果,证明了小波阈值去噪法是一种非常有效的变换域图像去噪方法。