Background: With recent technology, multivariate time-series electrocardiogram (ECG) analysis has played an important role in diagnosing cardiovascular diseases. However, discovering the association of wide range aging disease and chronic habit with ECG analysis still has room to be explored. This article mainly analyzes the possible relationship between common aging diseases or chorionic habits of medical record and ECG, such as diabetes, obesity, and hypertension, or the habit of smoking. Methods: In the research, we first conducted different ECG features, such as those of reduced binary pattern, waveform, and wavelet and then performed a k-means clustering analysis on the correlation between ECGs and the aforementioned diseases and habits, from which it is expected to find a firm association between them and the best characteristics that can be used for future research. Results: In summary, we discovered a weak and strong evidence between ECG and medical records. For strong evidence, most patients with diabetes are always assigned into a specified group no matter the number of classes in the k-means clustering, which means we can find their association between them. For weak evidence, smokers, obesity, and hypertension have less unique ECG feature vector, enabling clustering them into specific groups, so the ECGs might be used to identify smokers, obesity, and hypertension. It is also interesting that we found obesity and hypertension, which are thought to be related to cardiovascular system. However, they are not highly correlated in our clustering analysis, which might indirectly tell us that the impact of obesity and hypertension to our body is various. In addition, the clustering effect of waveform feature is better than the other two methods.

With the rapid economic growth in China over the last two decades, exploring the changes in the Chinese economy has attracted great attention from the research community. Among different economic clusters in China, the southern region represents the wealthiest region. Hence, it is essential to conduct an in-depth analysis to explore the region’s sustainability in its economy. This paper applies the economic complexity model to 22 major cities within the Guangdong–Hong Kong–Macao Greater Bay Area cluster. The study is based on seven industrial sectors. Revealed comparative advantage of different product sectors, similarities of product sector specialisation, diversity of the economic composition, and the association to the geographical location are investigated in this paper.

Background: 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.Methods: 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.Results: 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.Conclusions: The electrocardiogram diagnostic system performs satisfactorily in the cigarette smoking habit analysis task, and demonstrates that cigarette smoking is significantly associated with the electrocardiogram.