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首页> 外文期刊>BMC Medical Informatics and Decision Making >Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records
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Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records

机译：使用临床记录的时间序列心电图与吸烟的医疗保健知识

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In the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG). However, there is no formal proof of a significant relationship between cigarette smoking and electrocardiogram results. In this study, we therefore investigate and prove the relationship between electrocardiogram and smoking using unsupervised neural network techniques. In this research, a combination of two techniques of pattern recognition; feature extraction and clustering neural networks, is specifically investigated during the diagnostic classification of cigarette smoking based on different electrocardiogram feature extraction methods, such as the reduced binary pattern (RBP) and Wavelet features. In this diagnostic system, several neural network models have been obtained from the different training subsets by clustering analysis. Unsupervised neural network of clustering cigarette smoking was then implemented based on the self-organizing map (SOM) with the best performance. Two ECG datasets were investigated and analysed in this prospective study. One is the public PTB diagnostic ECG databset with 290 samples (age 17–87, mean 57.2; 209 men and 81 women; 73 smoking and 133 non-smoking). The other ECG database is from Taichung Veterans General Hospital (TVGH) and includes 480 samples (240 smoking, and 240 non-smoking). The diagnostic accuracy regarding smoking and non-smoking in the PTB dataset reaches 80.58% based on the RBP feature, and 75.63% in the second dataset based on Wavelet feature. The electrocardiogram diagnostic system performs satisfactorily in the cigarette smoking habit analysis task, and demonstrates that cigarette smoking is significantly associated with the electrocardiogram.
机译：在少数关于临床经验的研究中，吸烟可能与缺血性心脏病和急性冠状动脉事件有关，这可以反映在心电图（ECG）中。然而，没有正式证明吸烟和心电图之间具有重要关系。在这项研究中，我们因此调查和证明使用无监督的神经网络技术进行心电图和吸烟之间的关系。在本研究中，两种模式识别的组合识别;特征提取和聚类神经网络在基于不同的心电图特征提取方法的诊断分类期间，在诊断分类期间进行了研究，例如减少二元图案（RBP）和小波特征。在该诊断系统中，通过聚类分析从不同的训练子集中获得了几种神经网络模型。然后基于具有最佳性能的自组织地图（SOM）来实现无监督的聚类香烟吸烟网络。在这项前瞻性研究中调查并分析了两个ECG数据集。一个是具有290个样本的公共PTB诊断ECG数据库（17-87岁，平均57.2; 209名男性和81名女性; 73次吸烟和133名禁烟）。另一家ECG数据库来自台中退伍军人综合医院（TVGH），包括480个样品（240次吸烟，240名禁烟）。在PTB数据集中的吸烟和禁止吸烟的诊断准确性基于RBP功能达到80.58％，基于小波特征的第二个数据集中的75.63％。心电图诊断系统在香烟吸烟习惯分析任务中令人满意地表现出令人满意的，并表明吸烟与心电图显着相关。

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