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Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction
Data Technologies and Applications ( IF 1.6 ) Pub Date : 2020-10-26 , DOI: 10.1108/dta-03-2020-0076
Fuad Ali Mohammed Al-Yarimi , Nabil Mohammed Ali Munassar , Fahd N. Al-Wesabi

Purpose

Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.

Design/methodology/approach

Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.

Findings

From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.

Originality/value

The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.



中文翻译:

心电图流水平相关模式作为对心律进行分类以预测心律不齐的特征

目的

数字计算和机器学习驱动的预测分析在非传染性疾病的诊断中正变得越来越重要。在全球范围内,许多研究集中在开发用于这种检测的综合模型。在心律失常的建议诊断中,这是预防与心脏相关的死亡的关键诊断,从根本上讲,任何建设性模型都可以作为价值主张。在这项研究中,重点是开发一个整体系统,该系统可以根据给定的心电图报告预测心律失常的范围。所提出的方法以心电图元素的顺序模式为特征。

设计/方法/方法

考虑到现代分类方法的决策准确性(不足以在临床实践中使用),本手稿创造了使用AdaBoost分类器执行监督学习和分类的功能的新维度。该方法的标题为“心电图流水平相关特征作为特征(ESCPF)”,该方法将心电图(ECG)信号流作为输入记录,以执行基于学习的监督分类,以检测给定ECG记录中的心律失常范围。

发现

从研究结果和比较报告中可以看出,与某些早期模型相比,该模型的执行精度更高。但是,关注新兴的解决方案和技术,如果可以提高模型的准确性,则可以导致令人信服的预测和过程的准确结果。

创意/价值

作者代表完全自动和快速的心律失常作为分类器,可以在线应用并有效地检查长的心电图记录序列。通过释放提取特征的需求,作者基于原始信号(心率数据的一个结果)规划了一个应用程序,其目的是在达到最小分类错误结果时减少计算时间。

更新日期:2020-11-02
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