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. 2020 May 5:12:95.
doi: 10.3389/fnagi.2020.00095. eCollection 2020.

Clustering Analysis of Aging Diseases and Chronic Habits With Multivariate Time Series Electrocardiogram and Medical Records

Kuo-Kun Tseng  1 , Jiaqian Li  1 , Yih-Jing Tang  2   3 , Ching-Wen Yang  4 , Fang-Ying Lin  1 , Zhaowen Zhao  1
Affiliations

Clustering Analysis of Aging Diseases and Chronic Habits With Multivariate Time Series Electrocardiogram and Medical Records

Kuo-Kun Tseng et al. Front Aging Neurosci. .

Abstract

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.

Keywords: disease analysis; electrocardiogram; feature extraction; habit analysis; k-means clustering.

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Figures

FIGURE 1
FIGURE 1
Components of ECG signal.
FIGURE 2
FIGURE 2
Algorithms of ECG clustering analysis.
FIGURE 3
FIGURE 3
Process of RBP feature extraction.
FIGURE 4
FIGURE 4
Process of wavelet feature extraction.
FIGURE 5
FIGURE 5
Process of waveform feature extraction.
FIGURE 6
FIGURE 6
Percentage chart of clustering result: (A) RBP-based, (B) waveform-based, and (C) wavelet-based feature extractions.
See this image and copyright information in PMC

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