Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process

IF 0.6 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES Acta Scientiarum-technology Pub Date : 2022-07-28 DOI:10.4025/actascitechnol.v44i1.60386
B. R. Oliveira, M. A. Q. Duarte, J. Vieira Filho
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Abstract

Premature Ventricular Contractions (PVC) arrhythmias can be associated with sudden death and acute myocardial infarction, occurring in 50% of the population for Holter monitoring. PVC patterns are very hard to be recognized since their waveforms can be confused with other heartbeats, such as Right and Left Bundle Branch Blocks. This work proposes a new approach for PVC recognition, based on Gaussian Naive Bayes algorithm and AMUSE (Algorithm for Multiple Unknown Signal Extraction), which is a method for the blind source separation problem. This approach provides a set of attributes that are combined by Linear Discriminant Analysis, allowing the training of an ensemble learning. The Analytic Hierarchy Process weights each learned model according to its importance, obtained from the performance metrics. This approach has some advantages over baseline methods since it does not use a pre-processing stage and employs a simple machine learning model trained using only two parameters for each feature. Using a standard dataset for training and test phases, the proposed approach achieves 98.75% accuracy, 90.65% sensitivity, and 99.46% specificity. The best performance was 99.57% accuracy, 98.64% sensitivity, and 99.65% specificity for other datasets. In general, the proposed approach is better than 66% of the state-of-the-art methods concerning accuracy
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采用盲源分离和层次分析法加权的集合高斯朴素贝叶斯识别室性早搏
室性早搏(PVC)心律失常可与猝死和急性心肌梗死相关,发生在50%的动态心电图监测人群中。PVC模式很难识别,因为它们的波形可能与其他心跳相混淆,例如右束和左束分支块。本文提出了一种基于高斯朴素贝叶斯算法和AMUSE (algorithm for Multiple Unknown Signal Extraction)的PVC识别新方法,该方法是一种针对盲源分离问题的方法。这种方法提供了一组由线性判别分析组合的属性,允许集成学习的训练。层次分析法根据从性能指标中得到的重要程度对每个学习模型进行加权。这种方法比基线方法有一些优点,因为它不使用预处理阶段,并且使用简单的机器学习模型,每个特征只使用两个参数进行训练。使用训练和测试阶段的标准数据集,该方法达到98.75%的准确率,90.65%的灵敏度和99.46%的特异性。在其他数据集上,准确率为99.57%,灵敏度为98.64%,特异性为99.65%。总的来说,所提出的方法在准确性方面优于66%的最先进的方法
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来源期刊
Acta Scientiarum-technology
Acta Scientiarum-technology 综合性期刊-综合性期刊
CiteScore
1.40
自引率
12.50%
发文量
60
审稿时长
6-12 weeks
期刊介绍: The journal publishes original articles in all areas of Technology, including: Engineerings, Physics, Chemistry, Mathematics, Statistics, Geosciences and Computation Sciences. To establish the public inscription of knowledge and its preservation; To publish results of research comprising ideas and new scientific suggestions; To publicize worldwide information and knowledge produced by the scientific community; To speech the process of scientific communication in Technology.
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