School of Computer Science, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Department of Family Medicine, Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan.
BMC Med Inform Decis Mak. 2020 Jul 9;20(Suppl 3):127. doi: 10.1186/s12911-020-1107-2.
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.
在现有的少量临床经验研究中，吸烟可能与缺血性心脏病和急性冠状动脉事件有关，这些可以在心电图（ECG）中反映出来。然而，吸烟与心电图结果之间并没有显著关系的正式证明。因此，在这项研究中，我们使用无监督神经网络技术来研究和证明心电图与吸烟之间的关系。
在这项研究中，结合了两种模式识别技术；特征提取和聚类神经网络，在基于不同心电图特征提取方法（如简化二进制模式（RBP）和小波特征）的吸烟诊断分类中进行了专门研究。在这个诊断系统中，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，基于最佳性能的自组织映射（SOM）实现了聚类吸烟的无监督神经网络。
在这项前瞻性研究中，我们调查和分析了两个 ECG 数据集。一个是具有 290 个样本（年龄 17-87 岁，平均 57.2 岁；209 名男性和 81 名女性；73 名吸烟者和 133 名非吸烟者）的公共 PTB 诊断 ECG 数据集。另一个 ECG 数据库来自台中荣民总医院（TVGH），包括 480 个样本（240 名吸烟者，240 名非吸烟者）。基于 RBP 特征，PTB 数据集在吸烟和非吸烟的诊断准确性达到 80.58%，基于小波特征，第二个数据集达到 75.63%。
心电图诊断系统在吸烟习惯分析任务中表现良好，证明吸烟与心电图显著相关。
