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Extended Central Tendency Measure and difference plot for heart rate variability analysis
Rascius-Endrigho A U Belfort1, Sara P C Treccossi2, João L F Silva1
1Universidade do Vale do Paraíba, São José dos Campos, SP, Brazil.
Insights
The extended Central Tendency Measure (e-CTM) effectively analyzes heart rate variability (HRV) by incorporating long-term intervals. This method distinguishes between pathological autonomic nervous system (ANS) impairment and temporary stress-induced changes in HRV.
Area of Science:
- Cardiology
- Biomedical Engineering
- Physiology
Background:
- Heart rate variability (HRV) analysis assesses autonomic nervous system (ANS) function non-invasively.
- The Central Tendency Measure (CTM) quantifies RR interval variability but focuses on successive differences.
- Limitations exist in conventional CTM for detecting long-term ANS impairments.
Purpose of the Study:
- To introduce an extended Central Tendency Measure (e-CTM) for enhanced HRV analysis.
- To incorporate a new parameter in e-CTM for analyzing long-term RR interval variations.
- To evaluate the e-CTM's capability in differentiating ANS impairments.
Main Methods:
- Developed the extended Central Tendency Measure (e-CTM) by adding a parameter for long-term interval analysis.
- Collected 145 RR interval time series from three distinct groups: congestive heart failure patients, healthy individuals, and nurses.
- Applied e-CTM to differentiate between the groups based on HRV patterns.
Main Results:
- The e-CTM successfully differentiated RR interval time series between groups.
- The method distinguished subjects with pathological ANS impairment (congestive heart failure) from healthy controls.
- e-CTM also identified temporary ANS impairment in subjects under work-related stress (nurses).
Conclusions:
- The e-CTM is a valuable tool for analyzing long-term HRV variations.
- This extended method can detect and differentiate various forms of ANS impairment.
- e-CTM offers improved insights into cardiac autonomic regulation in health and disease.
Abstract:
Heart rate variability (HRV) is a non-invasive alternative to analyze the role of the autonomic nervous system (ANS) on heart functioning. Many tools have been developed to analyze collected cardiac data. Among them, the Central Tendency Measure (CTM) is a quantitative method for variability analysis of RR intervals. The values of the CTM must be between 0 and 1 (inclusive) for different radius, which follows the intrinsic characteristics of each time series. Using the conventional CTM, the successive differences of the time series may be calculated, and it can classify and differentiate the disturbances in the ANS involving HRV. This method was extended (e-CTM) to analyze the differences between RR interval time series. In this extension, a new parameter is added, which allows analysis of long time intervals, instead of successive and adjacent RR intervals. The ability of the e-CTM to differentiate the groups of the RR interval time series was verified with 145 RR interval time series divided into three groups: subjects with congestive heart failure, healthy subjects, and nurses during one hour of their workday. Results evidence that the new parameter added differentiates the group with pathology (and subsequent impairment of ANS) and group under stress at work (temporary impairment of ANS). These results suggest that the e-CTM is capable of detection long-term variations in the HRV according to the ANS impairment.
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