Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification

2021 ◽  
pp. 254-266
Author(s):  
Hanadi Aldosari ◽  
Frans Coenen ◽  
Gregory Y. H. Lip ◽  
Yalin Zheng
Keyword(s):  
Cardiovascular Disease ◽  
Time Series ◽  
Feature Vector ◽  
Disease Classification ◽  
Vector Representation ◽  
Data Heterogeneity

scholarly journals Patterns of Circadian Variation in 24-Hour Ambulatory Blood Pressure, Heart Rate, and Sympathetic Tone Correlate with Cardiovascular Disease Risk: A Cluster Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Myung Han Hyun ◽  
Jun Hyuk Kang ◽  
Sunghwan Kim ◽  
Jin Oh. Na ◽  
Cheol Ung Choi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Time Series ◽  
Circadian Variation ◽  
Sympathetic Tone ◽  
Atherosclerotic Cardiovascular Disease ◽  
Metabolic Factors ◽  
Multiple Variables ◽  
Ascvd Risk

To investigate whether specific time series patterns for blood pressure (BP), heart rate (HR), and sympathetic tone are associated with metabolic factors and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). A total of 989 patients who underwent simultaneous 24-hour ambulatory BP and Holter electrocardiogram monitoring were enrolled. The patients were categorized into sixteen groups according to their circadian patterns using the consensus clustering analysis method. Metabolic factors, including cholesterol profiles and apolipoprotein, were compared. The 10-year ASCVD risk was estimated based on the Framingham risk model. Overall, 16 significant associations were found between the clinical variables and cluster groups. Age was commonly associated with all clusters in systolic BP (SBP), diastolic BP (DBP), HR, and sympathetic tone. Metabolic indicators, including diabetes, body mass index, total cholesterol, high-density lipoprotein, and apolipoprotein, were associated with the four sympathetic tone clusters. In the crude analysis, the ASCVD risk increased incrementally from clusters 1 to 4 across SBP, DBP, HR, and sympathetic tone. After adjustment for multiple variables, however, only sympathetic tone clusters 3 and 4 showed a significantly high proportion of patients at high risk (≥7.5%) of 10-year ASCVD (odds ratio OR=5.90, 95% confidential interval CI=1.27–27.46, and P value = 0.024 and OR=15.28, 95% CI=3.59–65.11, and P value < 0.001, respectively). Time series patterns of BP, HR, and sympathetic tone can serve as an indicator of aging. Circadian variations in sympathetic tone can provide prognostic information about patient metabolic profiles and indicate future ASCVD risk.

scholarly journals F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
R. M. Dünki ◽  
M. Dressel
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Signal Analysis ◽  
Feature Vector ◽  
Spectral Power ◽  
Ratio Test ◽  
Test Statistics ◽  
Biomedical Signal ◽  
Statistical Assessment ◽  
Biomedical Signal Analysis

Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ2(F)/F2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

Chinese Person Name Disambiguation Based on Two-Stage Clustering

2016 ◽  
Vol 20 (5) ◽  
pp. 755-764
Author(s):  
Jie Zhou ◽  
◽  
Bicheng Li ◽  
Yongwang Tang ◽  
Keyword(s):  
High Precision ◽  
Feature Vector ◽  
Social Relation ◽  
Statistical Properties ◽  
Experimental Results ◽  
Vector Representation ◽  
Name Disambiguation ◽  
Two Stage ◽  
Different Types ◽  
Cluster Feature

Person name clustering disambiguation is the process that partitions name mentions according to corresponding target person entities in reality. The existed methods can not realize effective identification of important features to disambiguate person names. This paper presents a method of Chinese person name disambiguation based on two-stage clustering. This method adopts a stage-by-stage processing model to identify and utilize different types of important features. Firstly, we extract three kinds of core evidences namely direct social relation, indirect social relation and common description prefix, recognize document-pairs referring to the same person entity, and realize initial clustering of person names with high precision. Then, we take the result of initial clustering as new initial input, utilize the statistical properties of multi-documents to recognize and evaluate important features, and build a double-vector representation of clusters (cluster feature vector and important feature vector). Based on the processes above, the final clustering of person names is generated, and the recall of clustering is improved effectively. The experiments have been conducted on the dataset of CLP2010 Chinese person names disambiguation, and experimental results show that this method has good performance in person name clustering disambiguation.

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