Clustering Analysis of Aging Diseases and Chronic Habits With Multivariate Time Series Electrocardiogram and Medical Records - Tseng, Kuo-Kun; Li, Jiaqian; Tang, Yih-Jing; Yang, Ching-Wen; Lin, Fang-Ying; Zhao, Zhaowen (Frontiers in Aging Neuroscience, 2020)
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 paper mainly analyses the possible relationship between common aging diseases or chorionic habits of Medical Record and ECG, such as the diseases of diabetes, obesity and hypertension, or the habit of smoking. Whether there is a relationship between ECG and some common diseases and habits. Method: In the research, we firstly conducted different ECG features, such as those of reduced binary pattern (RBP), waveform, and wavelet, and then performed a k-means clustering analysis on the correlation between ECGs and the aforementioned diseases and habits, from which expected to find a firm association between them and the best characteristic used for future research. Result: In a summary, we discovered a weak and strong evidence between ECG and medical records, for the weak that the smokers might have a unique ECG feature vector enabling clustering them into specific groups, so the ECGs might be used to identify smokers and non-smokers. For a stronger evidence, most of patients with diabetes are always assigned into a specified group no matter of the number of classes in the K-means clustering, which means we can find their association between them. It is also interesting, we found that 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.