An overview on machine learning methods for ECG Heartbeat Arrhythmia Classification

Mohamed Sraitih, Y. Jabrane, Abdelghafour Atlas
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引用次数: 2

Abstract

The automated classifiers can assist cardiology experts in diagnosing heart-linked illnesses with the initial and well-predicted results of classified ECG signals and data of subjects. This work serves as a review of various research works on ECG data and signals classification using supervised machine learning approaches. We presented and reviewed, the performance and the strategies used to classify cardiac arrhythmias using five widely used classifiers, Support vector machine (SVM), Random forest (RF), Decision three (DT), Naïve Bayes (NB), and K-nearest neighbour (KNN). And we discussed a variety of limitations and revealed that there is still area for increase in the classification's performance, specially by minimizing the preprocessing and feature extraction step which can cause an important computational cost and different accuracy outcomes. Such a coordinated research review allows scientists to merge an unobstructed view on some aspects of ECG classification for the identification of the research issues unmet so far. We plan to consider the obstacles discussed and some limitations to further raise the classification performance of the automated classifiers for the applicability of such solutions in clinical diagnosis.
心电图心律不齐分类的机器学习方法综述
自动分类器可以帮助心脏病专家通过分类心电图信号和受试者数据的初始和良好预测结果来诊断心脏相关疾病。这项工作是对使用监督机器学习方法的ECG数据和信号分类的各种研究工作的回顾。我们介绍并回顾了使用支持向量机(SVM)、随机森林(RF)、决策三(DT)、Naïve贝叶斯(NB)和k近邻(KNN)这五种广泛使用的分类器对心律失常进行分类的性能和策略。我们讨论了各种限制,并揭示了分类性能仍有提高的空间,特别是通过最小化预处理和特征提取步骤,这可能会导致重要的计算成本和不同的精度结果。这样一个协调的研究综述使科学家能够对ECG分类的某些方面进行无阻碍的合并,以确定迄今为止尚未解决的研究问题。我们计划考虑所讨论的障碍和一些限制,以进一步提高自动分类器的分类性能,使这些解决方案在临床诊断中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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