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Identifying increased risk of post-infarct people with diabetes using multi-lag Tone-Entropy analysis
Chandan Karmakar1, Herbert Jelinek, Ahsan Khandoker
1Electrical & Electronic Engineering Department, University of Melbourne, Parkville, Melbourne, VIC 3010, Australia. karmakar@unimelb.edu.au
Insights
A new tone-entropy algorithm improves cardiac mortality risk prediction in diabetes by analyzing heart rate variability at longer intervals. This method better identifies risks associated with cardiac autonomic neuropathy.
Area of Science:
- Cardiology
- Diabetology
- Autonomic Nervous System Research
Background:
- Diabetes mellitus causes multi-organ dysfunction, including cardiovascular and nervous system damage.
- Cardiac autonomic neuropathy (CAN), a complication of diabetes, is linked to reduced heart rate variability (HRV) and increased sudden cardiac death risk.
- Existing HRV algorithms focus on beat-to-beat variations, potentially missing crucial predictive information for mortality risk.
Purpose of the Study:
- To evaluate a novel tone-entropy algorithm incorporating extended lag intervals for improved cardiac mortality risk assessment in diabetic individuals.
- To determine if analyzing HRV at larger lag intervals enhances the identification of sympatho-vagal balance and total activity changes relevant to mortality risk.
Main Methods:
- Application of a novel tone-entropy algorithm with increased lag intervals to analyze heart rate variability data.
- Comparison of the novel algorithm's performance against traditional HRV analyses in predicting cardiac mortality risk.
- Assessment of changes in sympatho-vagal balance and total activity at various lag intervals.
Main Results:
- The tone-entropy algorithm demonstrated significant changes in sympatho-vagal balance and total activity at larger lag intervals.
- The novel tone-entropy algorithm proved to be a superior predictor of cardiac mortality in diabetic patients compared to standard HRV methods, especially at lag intervals greater than one.
- Optimal risk identification was achieved at lag seven using the tone-entropy algorithm.
Conclusions:
- The enhanced tone-entropy algorithm, by incorporating larger lag intervals, offers a more sensitive and accurate method for assessing cardiac mortality risk in diabetes.
- This advanced HRV analysis provides deeper insights into the autonomic nervous system's impact on cardiac health in diabetic populations.
- The findings suggest a potential clinical application of this novel algorithm for proactive risk stratification and management of cardiovascular complications in diabetes.
Abstract:
Diabetes mellitus is associated with multi-organ system dysfunction. One of the key causative factors is the increased blood sugar level that leads to an increase in free radical activity and organ damage including the cardiovascular and nervous system. Heart rhythm is extrinsically modulated by the autonomic nervous system and cardiac autonomic neuropathy or dysautonomia has been shown to lead to sudden cardiac death in people with diabetes due to the decrease in heart rate variability (HRV). Current algorithms for determining HRV describe only beat-to-beat variation and therefore do not consider the ability of a heart beat to influence a train of succeeding beats. Therefore mortality risk analysis based on HRV has often not been able to discern the presence of an increased risk. This study used a novel innovation of the tone-entropy algorithm by incorporating increased lag intervals and found that both the sympatho-vagal balance and total activity changed at larger lag intervals. Tone-Entropy was found to be better risk identifier of cardiac mortality in people with diabetes at lags higher than one and best at lag seven.
