Asymmetric spread of heart rate variability

Rohila, Ashish; Sharma, Ambalika

doi:10.1016/j.bspc.2020.101985

Cited by 9 publications

(9 citation statements)

References 38 publications

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“…Our results reveal that lower short-term HRA is manifested in athletes compared to non-athletes. Rohila and Sharma reported that asymmetry in subjects with congestive heart failure and arrhythmia was higher than normal sinus rhythm [26]. They proposed that an ASI value of 50 indicates a balanced response by both sympathetic tone and parasympathetic tone.…”

Section: Discussionmentioning

confidence: 99%

“…If a point is above the LI, the associated slope angle is positive, and the corresponding slope angle is negative below the LI. Finally, the SI was calculated by dividing the total of positive slope angles by the sum of all slope angles [25,26] as follows:…”

Section: Slope Index (Si)mentioning

confidence: 99%

“…After this, the arc length of each scatter point was calculated as a i = θ i •d i . The ASI was calculated by dividing the standard deviation of arc length for points above the LI by twice the standard deviation of arc length for all points, excluding those on the LI [26]. ASI is defined mathematically as:…”

Section: Asymmetric Spread Index (Asi)mentioning

confidence: 99%

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Analysis of Short-Term Heart Rate Asymmetry in High-Performance Athletes and Non-Athletes

et al. 2022

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Heart rate asymmetry (HRA) refers to how asymmetrically the acceleration and deceleration patterns in heartbeat fluctuations are distributed. There is limited evidence regarding HRA changes in athletes and their association with autonomic regulation. This study aimed to compare the short-term HRA of high-performance athletes and non-athletes during an autonomic function test by calculating relevant HRA measures. This exploratory study obtained beat-to-beat RR interval time series from 15 high-performance athletes and 12 non-athletes during a standardized autonomic function test. This test includes rest, postural change, controlled respiration, prolonged orthostatism, exercise, and recovery phases. The following HRA parameters were computed from the RR time series for both groups: asymmetric spread index (ASI), slope index (SI), Porta’s index (PI), Guzik’s index (GI), and Ehlers’ index (EI). We found significant differences (p < 0.01) in the mean value of several HRA parameters between athletes and non-athletes and across the autonomic function test phases, mainly in postural change and recovery phases. Our results indicate that high-performance athletes manifest a higher number and magnitude of cardiac decelerations than non-athletes after an orthostatic challenge, as indicated by GI and EI. In addition, lower HRA was found in athletes in the recovery phase than in non-athletes, as indicated by ASI.

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Section: Discussionmentioning

confidence: 99%

Section: Slope Index (Si)mentioning

confidence: 99%

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Analysis of Short-Term Heart Rate Asymmetry in High-Performance Athletes and Non-Athletes

et al. 2022

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“…This is consistent with previous studies that AI has better stability and consistency for short-term time series and cardiovascular disease causes AI to increase [ 15 , 31 ]. The values of HRA indices were increased in pathological situations [ 14 , 36 , 37 ], while researchers found the lower irreversibility measures mean a loss of complexity and pathological states [ 38 , 39 ]. Time irreversibility means that the statistical characteristics of the signal are not invariant when time reversal, while asymmetry refers to the distribution of the signal is imbalanced and/or disproportionate [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Short-Term Effect of Percutaneous Coronary Intervention on Heart Rate Variability in Patients with Coronary Artery Disease

Yan

Liu

Yao

et al. 2021

Entropy

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Myocardial ischemia in patients with coronary artery disease (CAD) leads to imbalanced autonomic control that increases the risk of morbidity and mortality. To systematically examine how autonomic function responds to percutaneous coronary intervention (PCI) treatment, we analyzed data of 27 CAD patients who had admitted for PCI in this pilot study. For each patient, five-minute resting electrocardiogram (ECG) signals were collected before and after the PCI procedure. The time intervals between ECG collection and PCI were both within 24 h. To assess autonomic function, normal sinus RR intervals were extracted and were analyzed quantitatively using traditional linear time- and frequency-domain measures [i.e., standard deviation of the normal-normal intervals (SDNN), the root mean square of successive differences (RMSSD), powers of low frequency (LF) and high frequency (HF) components, LF/HF] and nonlinear entropy measures [i.e., sample entropy (SampEn), distribution entropy (DistEn), and conditional entropy (CE)], as well as graphical metrics derived from Poincaré plot [i.e., Porta’s index (PI), Guzik’s index (GI), slope index (SI) and area index (AI)]. Results showed that after PCI, AI and PI decreased significantly (p < 0.002 and 0.015, respectively) with effect sizes of 0.88 and 0.70 as measured by Cohen’s d static. These changes were independent of sex. The results suggest that graphical AI and PI metrics derived from Poincaré plot of short-term ECG may be potential for sensing the beneficial effect of PCI on cardiovascular autonomic control. Further studies with bigger sample sizes are warranted to verify these observations.

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“…This study is the first that examined the relationships between HRA and HRF indices and therefore, the specific physiological mechanisms underlying their close proximity should be further investigated. Nevertheless, as existing literature has highlighted the diagnostic values of both indices, especially for cardiac disorders (Bergfeldt & Haga, 2003;Costa et al, 2017Costa et al, , 2018Costa & Goldberger, 2019;Guzik et al, 2013;Karmakar et al, 2012;Rohila & Sharma, 2020b) proximity (Captur et al, 2017;Francis et al, 2002;Lensen 365 et al, 2020;Young & Benton, 2015). Specifically, the health or demographic variables).…”

Section: Discussionmentioning

confidence: 99%

Unveiling the Structure of Heart Rate Variability (HRV) Indices: A Data-driven Meta-clustering Approach

Pham¹,

Lau²,

Chen³

et al. 2021

Preprint

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Heart Rate Variability (HRV) can be estimated using a myriad of mathematical indices, but the lack of systematic comparison between these indices renders the interpretation and evaluation of results tedious. In this study, we assessed the relationship between 57 HRV metrics collected from 302 human recordings using a variety of structure-analysis algorithms. We then applied a meta-clustering approach that combines their results to obtain a robust and reliable view of the observed relationships. We found that HRV metrics can be clustered into 3 groups, representing the core variability features, extreme variability features and frequency/complexity features. From there, we described and discussed their associations, and derived recommendations on which indices to prioritize for parsimonious, yet comprehensive HRV-related data analysis and reporting.

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Asymmetric spread of heart rate variability

Cited by 9 publications

References 38 publications

Analysis of Short-Term Heart Rate Asymmetry in High-Performance Athletes and Non-Athletes

Analysis of Short-Term Heart Rate Asymmetry in High-Performance Athletes and Non-Athletes

Short-Term Effect of Percutaneous Coronary Intervention on Heart Rate Variability in Patients with Coronary Artery Disease

Unveiling the Structure of Heart Rate Variability (HRV) Indices: A Data-driven Meta-clustering Approach

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