Application of Time Series Analysis to Clinical Data (Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP))

Abstract

Multivariable analysis methods are frequently used in studies in the field of health carried out through the variables such as heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP), etc. In this respect, the basic purpose of this study is to demonstrate that it is more appropriate to analyze the clinical variables that change over time with time series analysis. Data used in the study were obtained from twenty-four-hour rhythm and blood pressure results of holter monitor worn by the patients who have consulted cardiology policlinic with the complaint of blood pressure and heart attack. Heart rate rates (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) variables were obtained from the appropriate 250 files. According to the results, there is a causal relationship between HR with SBP and DBP for male and female patients. The p values are 0.0017 and 0.0084 for males and 0.0056 and 0.0001 for females, respectively. This result shows that SBP and DBP can be used to predict HR. According to the results of the time series analysis, it is shown that HR and SBP and DBP variables are correlated but correlations are immediate, and stabilized over time. In our study, it has been shown that applying time series analysis for the time-varying data will give more detailed results.

Keywords

• Time series
• Cointegration
• Granger causality

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