Abstract
Background:
Phase-Rectified Signal Averaging (PRSA) methods, Deceleration (DC), and Acceleration Capacity (AC), provide a comprehensive assessment of cardiac autonomic function (CAN). There are no published studies comparing PRSA methods with conventional methods such as Time Domain Heart Rate Variability (TD-HRV) or Cardiac Autonomic Reflex Tests (CARTs), nor have they described a cut-off value for AC and DC to distinguish patients with CAN (+ve) from those without CAN (-ve). Our study compares PRSA methods with conventional methods and defines cut-off values for AC and DC to diagnose CAN.
Methodology:
We studied two cohorts: 126 individuals with normal ventricular function (derivation cohort) and 143 individuals with Left Ventricular Dysfunction (validation cohort). These patients underwent CARTs and supine, resting ECG recordings for 2 to 3 minutes. The patients were categorized as CAN +Ve and CAN -Ve based on TD-HRV parameters and the CARTs. Two different CART criteria were studied: the All-India Institute of Medical Sciences (AIIMS-AFT) criteria and the 2011 Toronto Consensus recommendations. Patients with and without CAN were segregated by AC and DC values, and the methods were compared. The cutoff values for DC and AC were calculated using the ROC curve method from the derivation cohort and verified in the validation cohort.
Results:
A reduction in DC values and an increase in AC values indicate a higher chance of CAN. The cut-off values of -7 for AC and 7 for DC provide the highest accuracy in detecting CAN prevalence. Both values have an AUC of nearly 0.9. Reclassifying both Cohorts as CAN +Ve based on the derived cut-offs and comparing with the prevalence determined by conventional methods results in kappa values ranging from 0.5 to 0.7.
Conclusions:
A decrease in DC value and an increase in AC value are associated with a higher probability of CAN. An AC value ≥ -7 and a DC value ≤ 7 indicate good accuracy in identifying CAN.
Keywords: Cardiac Autonomic Function, Phase-Rectified Signal Averaging, Deceleration capacity, Acceleration capacity, Heart Rate Variability, Cardiac, Autonomic Reflex Tests
Introduction
In 2006, Axel Bauer et al. [1] described the RR interval processing technique, phase-rectified signal averaging (PRSA), to calculate the Deceleration capacity (DC) and Acceleration capacity (AC). After 2006, multiple studies on DC and AC in different patient groups, such as diabetes [2], heart failure [3,4], stroke [5], and other conditions [6,7], have been published.
These studies have confirmed the utility of PRSA methods for CAN evaluation; however, no studies have compared them with conventional methods such as TD-HRV or gold-standard CARTs.
The present study assesses the comparative performance of both PRSA methods, DC and AC, with conventional common method TD-HRV and the ‘gold standard’ CARTs in two different populations, those with normal Left Ventricular function (NLV) and those with left ventricular dysfunction (LVD). The cutoff values for DC and AC to define CAN +Ve are determined using the ROC curves.
Though 24-hour ECG recordings are the norm for PRSA analysis, few studies used short- duration ECG recordings such as 30 minutes [8] and 5 minutes [9]. We used ultra-short-duration recordings (2-3 minutes) for DC and AC calculations. This reduction in ECG recording duration represents a paradigm shift, as DC and AC calculations become simpler, reducing patient burden and cost.
Standard and non-standard abbreviations.
PRSA-Phase Rectified Signal Averaging, AC: Acceleration Capacity, DC: Deceleration Capacity.
Aims of the study.
To assess the comparative performance of AC and DC with Time-Domain Heart Rate Variability (TD-HRV) and Cardiac Autonomic Reflex Tests (CARTs) in detecting the prevalence of Cardiac Autonomic Neuropathy (CAN).
Methods
This study was conducted in accordance with the Declaration of Helsinki. The institutional ethics committee (Little Flower Hospital and Research Centre, Angamaly, Kerala) approved the CAN-HF study protocol (EC/01/2023) on 29.03.2023. This analysis is based on data from the CAN-HF study.
The study was conducted at Little Flower Hospital and Research Centre, Angamaly, Kerala, and the recruitment period was from 01.09.2023 to 31.03.2025. The study included two cohorts: first, healthy individuals aged 30 to 75 years with normal ventricular function and no structural heart disease, who underwent radial coronary angiography for various reasons. In this cohort, patients with a prior history of acute coronary syndrome and >50 % epicardial coronary artery disease were excluded. The second cohort included patients with Left Ventricular dysfunction irrespective of coronary artery disease. They were recruited based on a recent (< 1 month) Echocardiographic report with an Ejection Fraction <50% or an Average Global Longitudinal Strain (GLS) <16%. All consecutive patients were recruited after providing written informed consent. Patients with seizure disorder, neurodegenerative diseases, eGFR <30 mL/mt/1.73m2, and chronic pulmonary disease were excluded.
Data collection.
After obtaining written informed consent, the study team trained participants in performing each component of the CARTs. After the initial training and rest period, ECG and blood pressure data were recorded for two to three minutes on a Philips Intellivue MP20-MP90 monitor. The Medicollector software (MediCollector Inc., Boston, MA, United States) [10] captured the ECG signals at 500 Hz, and the blood pressure signals at 125 Hz, and transferred the data to Excel format. If data quality was not satisfactory, repeat sampling was performed until a high-quality trace was obtained. We developed two dedicated software programs: one to detect ‘NN’ intervals and another to calculate AC and DC. The detailed descriptions are given in Supplementary file -1. The steps for calculating AC and DC are as described by Axel Bauer [1], but instead of a 24-hour ECG recording, we used ultra-short-duration ECG data (2-3 minutes). After validating both the software in an adequate number of trial runs, we used them in the study. The ECG recordings at 500 Hz provided this protocol with 2-millisecond precision. After procuring clean baseline tracings, the cardiovagal tests were performed.
Data cleaning and editing.
The electronic data in Excel format was spread as ECG complexes and visually inspected. Non-sinus beats, or artifacts, were edited by appropriate methods as described, either by deletions or interpolations [11,12]. Interpolation was used to edit premature atrial and ventricular complexes. Repeat sampling for artifact-free data reduced the need for editing, with <1% of complexes requiring editing, which is significantly lower than the standard recommendation of 20% or less [13].
Three CAN definitions were used to assess the prevalence of CAN. Two are CART definitions, both being modifications of Ewing’s original criteria [14]: 1) Toronto Consensus Panel recommendations on Diabetic Neuropathy (2011) [15], and 2) All India Institute of Medical Science- Autonomic Function Test laboratory criteria (AIIMS-AFT) [16]. The CART manoeuvres were performed per the recommendations of the Diabetic Neuropathy Study Group of the Italian Society of Diabetology [17]. The third definition was based on TD-HRV. The age-specific cutoff value published by Andreas Voss [18] is used as the reference value. (Review of the three criteria in Supplementary file - 2).
Both cohorts were analysed for CAN using the three criteria, and median AC and DC values were calculated for those with and without CAN. A cutoff value for AC and DC to discriminate CAN + Ve was obtained from the derivation cohort (patients with normal LV function), and this value was verified in the validation cohort (patients with LV Dysfunction).
Statistical analysis.
Statistical analyses were conducted using SPSS Version 20.0 for Windows (IBM Corporation, ARMONK, NY, USA). Categorical variables were presented as numbers and percentages, and continuous variables as medians and interquartile ranges (IQRs). The Mann-Whitney test was used to compare AC and DC values between the two cohorts and subgroups. The receiver operating characteristic (ROC) curve method was used to determine the cutoff values of AC and DC for the CAN +ve and CAN-ve groups, and to calculate the area under the curve (AUC). An interrater reliability analysis using the Kappa statistic was performed to determine consistency among criteria. Sensitivity, Specificity, Positive Predictive value, Negative Predictive value, False positive rate, False negative rate, and Accuracy were calculated.
Results
309 individuals consented to participate in the study. After elimination of poor ECG tracings, poor participant cooperation during different study manoeuvres, and incomplete data, 126 participants’ data were available for analysis in the derivation cohort and 143 in the validation cohort.
The participants were adults and seniors aged 32-75 years, with a mean (± SD) age of 55.9 ± 8.5 years for those with normal LV function and 58.3 ± 9 years for those with LV dysfunction. The majority in the normal LV function group were females (69.8%), while males predominated (74.1%) in the LV dysfunction group. Supplementary file - 3 shows the demographic profile of participants.
The CAN prevalence varied according to different criteria. Patients with LV dysfunction had a higher prevalence of CAN across all criteria (Table 1). CAN prevalence determined by AIIMS criteria is the most specific, while that by Toronto-2011 is the most sensitive.
Table 1:
CAN prevalence by different criteria. pCAN- parasympathetic cardiac autonomic Neuropathy.
| Criteria Studied | Cohort-1 (126 participants with Normal LV Function) | Cohort-2 (143 participants with LV Dysfunction) | ||||
|---|---|---|---|---|---|---|
| Definite pCAN | Early pCAN | Total pCAN | Definite pCAN | Early pCAN | Total pCAN | |
| Toronto 2011 | 22 (17.5) | 21 (16.7) | 43 (34.2) | 48 (34.5) | 40 (28.8) | 88(63.3) |
| AIIMS | 3 (2.4) | 24 (19.0) | 27 (21.4) | 19 (14.1) | 35 (25.9) | 54 (40) |
| HRV | 38 (30.2) | 74 (51.7) | ||||
Median values of AC and DC in two cohorts.
CAN+ve patients have higher AC and lower DC values than those without (Supplementary file – 2, Tables S2 and S3). The median AC values were higher, and the median DC values were lower in the LV dysfunction cohort compared with those with normal LV function; this difference persisted in the CAN - Ve patients but was not statistically significant in the CAN +Ve patients when analysed by CARTs (Table 2, marked *).
Table 2.
Median value comparison between two cohorts, in those with and without CAN.
| Criteria | Normal LV Function Median (IQR) | LV Dysfunction (Validation) Median (IQR) | P value | |
|---|---|---|---|---|
| AC | Overall | -8.62 (-12.90, -5.44) | -5.20 (-7.89, -3.13) | <0.001 |
| Toronto-CAN -Ve | -10.41 (-14.73, -7.25) | -7.48 (-11.45, -5.60) | 0.002 | |
| AIIMS CAN -Ve | -9.46 (-13.99, -6.10) | -7.10 (-10.83, -4.63) | 0.001 | |
| HRV CAN -Ve | -9.80 (-14.61, -7.32) | -7.25 (-11.31, -5.67) | 0.001 | |
| Toronto CAN +Ve | -5.23 (-6.64, -3.00) | -3.99 (-6.57, -2.60) | 0.159 * | |
| AIIMS CAN + Ve | -4.81 (-6.70, -3.00) | -3.58 (-5.29, -2.21) | 0.046 * | |
| HRV CAN +Ve | -5.27 (-6.07, -3.28) | -3.64 (-5.14, -2.24) | 0.003 | |
| DC | Overall | 8.25 (5.19, 11.77) | 5.57 (3.18, 7.68) | <0.001 |
| Toronto-CAN -Ve | 10.23 (7.69, 14.28) | 7.43 (5.87, 12.05) | 0.006 | |
| AIIMS CAN -Ve | 9.25 (6.26, 13.72) | 7.05 (4.98, 9.91) | 0.001 | |
| HRV CAN -Ve | 9.90 (7.81,14.24) | 7.43 (5.86, 11.70) | 0.002 | |
| Toronto CAN +Ve | 4.66 (3.25, 6.26) | 4.07 (2.67, 6.28) | 0.301 * | |
| AIIMS CAN + Ve | 4.46 (3.06, 5.81) | 3.57 (2.38, 5.48) | 0.123 * | |
| HRV CAN +Ve | 4.94 (3.25, 5.77) | 3.55 (2.40,5.48) | 0.015 |
Segregation of CAN +ve based on AC and DC values.
Patients with CAN +ve and CAN -ve were segregated against each AC and DC value. (Patients segregated against each AC and DC values, and CAN +ve and CAN -ve patients against AC/DC values are shown in supplementary file-4). The Positive Predictive Value (PPV) of CAN increases with a reduction in DC values and an increase in AC values. Almost all patients with AC values > -3 and DC values <3 have CAN.
Cut-off values for AC and DC:
Based on the CAN prevalence data using the above three criteria, cut-off values for AC and DC are derived using the ROC curve method (Table 3).
Table 3:
Cutoff values for AC and DC. The receiver operating characteristic (ROC) curve method is used to detect the cut - off values and the area under the curve (AUC).
| Basic Criteria | Cut off value | Sensitivity | Specificity | AUC (95% CI) | p-value |
|---|---|---|---|---|---|
| Acceleration Capacity | |||||
| Toronto 2011 | -6.740 | 81.4% | 79.5% | 0.898 (0.844-0.951) | <0.001 |
| AIIMS | -7.940 | 92.6% | 64.6% | 0.841 (0.765-0.917) | <0.001 |
| HRV | -6.715 | 89.5% | 80.7% | 0.887 (0.829-0.945) | <0.001 |
| Deceleration Capacity | |||||
| Toronto 2011 | 7.285 | 77.1% | 83.7% | 0.901 (0.847-0.956) | <0.001 |
| AIIMS | 7.935 | 65.7% | 96.3% | 0.864 (0.791-0.937) | <0.001 |
| HRV | 7.285 | 78.4% | 94.7% | 0.910 (0.859-0.960) | <0.001 |
The cutoff values varied across criteria, and all had high AUCs (around 0.9). (ROC curve graphs in Supplementary file 5). When rounded to the nearest whole number, the cutoff values for AC were -7 and - 8, and for DC, they were 7 and 8. The AC cut-off values showed high sensitivity, while the DC cut-off values showed high specificity.
Validation of the AC and DC cut-off values:
The newly derived cut-off values were used to reclassify both the cohorts as CAN +ve and CAN -ve. The prevalence is given in Supplementary file 2, Table S1. The prevalence of CAN, determined by AC and DC values, was validated against the three conventional CAN criteria mentioned (HRV- based, Toronto-2011, and AIIMS criteria). The accuracy of the new classification for both cohorts is shown in Table 4, and the intertest agreement in the LV dysfunction Cohort is shown in Table-5. The accuracy and intertest agreement were marginally better for the AC value of -7 and DC value 7.
Table 4.
Accuracy comparison of different cut-off values.
| Basic Criteria | AC-7 | AC-8 | DC 7 | DC 8 | |
|---|---|---|---|---|---|
| Normal LV Function (Cohort-1) | |||||
| Toronto-2011 | 78.6 % | 78.6 % | 78.6 % | 80.2 % | |
| AIIMS | 70.6 % | 70.6 % | 73.8 % | 70.6 % | |
| HRV | 81.0 % | 81.0 % | 84.1 % | 79.4 % | |
| Average Accuracy | 76.73 % | 76.73 % | 78.83 % | 76.73 % | |
| LV Dysfunction (Cohort-2) | |||||
| Toronto-201 | 74.1 % | 73.4 % | 77.7 % | 73.4 % | |
| AIIMS | 68.9 % | 62.2 % | 66.7 % | 59.3 % | |
| HRV | 74.8 % | 71.3 % | 76.9 % | 69.9 % | |
| Average Accuracy | 72.6% | 69 % | 73.8% | 67.5% | |
Table-5.
Inter-test agreement (kappa values) of CAN prevalence by the AC/DC cut-off values in the LV dysfunction Cohort.
| Criteria | AC-7 Kappa (P value) | DC 7 Kappa (P value) | AC-8 Kappa (P value) | DC 8 Kappa (P value) |
|---|---|---|---|---|
| Toronto (Total pCAN) | 0.433 (<0.001) | 0.510 (<0.001) | 0.378 (<0.001) | 0.367 (<0.001) |
| AIIMS (Total pCAN) | 0.413 (<0.001) | 0.373 (<0.001) | 0.316 (<0.001) | 0.271 (<0.001) |
| HRV | 0.491 (<0.001) | 0.533 (<0.001) | 0.416 (<0.001) | 0.386 (<0.001) |
Discussion
The Acceleration and Deceleration capacity give an overall impact of autonomic influence [19] and are early indicators of autonomic imbalance [20].
Following the publication of the foundational study on AC and DC by Axel Bauer et al. in 2006, multiple studies have examined the utility of AC and DC in various disease conditions [1,2,3,4,5,7,8,20,21,22,23,24,25,26,27] (Supplementary Table S3). No studies have compared the validity of PRSA methods AC and DC with conventional TD-HRV or CARTs.
CAN is a progressive disorder; all markers of this pathophysiology behave as continuous variables and are considered abnormal when they exceed specific cut-off values. There are multiple diagnostic criteria for CAN that evaluate different physiologic aspects [15]. Different criteria are considered complementary rather than mutually exclusive in diagnosing CAN [15]. CARTs, the ‘gold standard’ specific criteria, are based on a set of manoeuvres and diagnose CAN at a later definitive stage, while HRV is an early marker [28].
Both cohorts in the study have a higher prevalence of risk factors for CAN, which accounts for the higher prevalence of CAN across all criteria. Both AC and DC are indices of Heart Rate variability, so a lower absolute numerical value means lower Heart rate variability. As AC has negative values, higher values are abnormal, while lower values are abnormal in the Deceleration capacity.
PRSA methods are earlier markers of CAN.
The median AC values were higher, and the median DC values were lower in the LV Dysfunction cohort compared to the NLV cohort. In the subcategory analysis based on CARTs, these variations remained significant in the CAN -Ve patients but became insignificant in CAN +Ve patients. As markers of CAN, higher AC values and lower DC values are expected in CAN +Ve patients. Given that LV dysfunction is a strong predictor of CAN development, higher AC and lower DC values are expected in patients with LV dysfunction. CARTs diagnose CAN at a later, more definitive stage, by which time AC and DC values have dropped so low that differences between groups are no longer apparent. In the cohort with a higher risk of CAN (LV Dysfunction cohort), patients with CAN-negative results by traditional CARTs have higher median AC values and lower median DC values. This demonstrates that AC and DC are sensitive, early markers of CAN, identifying dysfunction before conventional CARTs.
PRSA methods and conventional methods.
The PRSA methods have been validated as a risk stratification tool; the data show that Deceleration Capacity (DC) is more robust than Acceleration Capacity (AC) [1,24,25]. In the LV dysfunction cohort, the 25th percentile value for DC in those with CAN is ≈ 2.5 (varies from 2.38 to 2.67), and the 75th percentile value for AC is ≈ -2.5 (varies from -2.21 to -2.6). The 25th percentile value for DC corresponds to the high-risk category (DC ≤ 2.4), as described by Axel Bauer [1].
In both cohorts, segregation of CAN+ve and CAN-ve patients based on AC and DC values confirms that as DC values decrease and AC values increase, the PPV of CAN increases, but at the expense of reduced sensitivity. Almost all patients with AC values > -3 and DC values <3 have CAN, but these low cut-off values have low sensitivity and miss the majority of patients with CAN diagnosed by any CART or HRV criteria.
Ultra-short duration ECG recordings for PRSA methods.
Studies have validated ultra-short-duration (<5 minutes) ECG recordings for TD-HRV analysis. Most of the PRSA studies used 24-hour recordings; however, short-duration recordings have also been validated [8,9]. In the KORA-KMC study, a 5-minute recording was analysed [9]. The expected pattern of AC and DC value variation between normal LV function and those with LV Dysfunction, and between CAN +Ve and CAN -Ve patients, in the present study validates that ultra-short-duration ECG recordings are feasible for evaluating AC and DC values. The reduction in ECG recording duration represents a paradigm shift, as DC and AC calculations become simpler, thereby improving editing accuracy, reducing patient burden, and lowering costs. Consequently, DC and AC become valuable CAN testing tools for large-scale population-based studies and for routine clinical practice.
Cut-off values for PRSA methods to diagnose CAN.
The literature search does not identify a study that defines AC and DC cutoff values to discriminate CAN+ve patients from the -ve population. The cutoff values obtained in the present analysis showed only marginal variation across criteria, and all had high AUCs (around 0.9). Both the cutoff values were validated by assessing accuracy and intertest agreement. They showed an overall accuracy ≈ 70% in both cohorts, but it was marginally higher for an AC value of -7 and a DC value of 7. In the analysis of intertest agreement in the validation cohort, the cut-off values of -7 for AC and 7 for DC performed better, though the agreement was only moderately strong (maximum kappa value = 0.533). The segregation graph shows that, at this cutoff value, there are significant false positives when analyzed by conventional methods. The conventional methods of CAN detection and PRSA methods analyse different aspects of CAN, which explains the relatively poor correlation between tests.
Existing knowledge indicates that PRSA methods are markers of CAN and valid tools for risk stratification. The present study establishes PRSA as a tool for assessing CAN prevalence by defining a cutoff value.
Conclusions
Alterations in AC and DC are earlier markers of Cardiac Autonomic Neuropathy than CARTs. The prevalence of CAN determined by PRSA methods is not directly comparable to that identified by conventional methods such as TD-HRV and CARTs. A decrease in DC value and an increase in AC value are associated with a higher probability of CAN. An AC value ≥ -7 and a DC value ≤ 7 demonstrates good accuracy in identifying CAN. However, the prevalence identified with these cut-off values shows only moderate intertest agreement with conventional methods.
Limitations of the study.
It is a single-center study.
References
- 1.Axel Bauer, Jan W Kantelhardt, Petra Barthel, Raphael Schneider, Timo Mäkikallio, Kurt Ulm, Katerina Hnatkova, Albert Schömig, Heikki Huikuri, Armin Bunde, Marek Malik, Georg Schmidt. Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. Lancet 2006; 367: 1674–81.doi: 10.1016/S0140-6736(06)68735-7. [DOI] [PubMed] [Google Scholar]
- 2.Xing-De Wang, Li Zhou, Chao-Yu Zhu, Bin Chen, Zhong Chen, Li Wei. Autonomic function, as indicated by heart rate deceleration capacity and deceleration runs, in type 2 diabetes patients with or without essential hypertension. Clin Interv Aging 2018; 13 1169–1176. 10.2147/CIA.S149920 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wei Hu, Xian Jin, Peng Zhang, Qiang Yu, Guizhi Yin, Yi Lu, Hongbing Xiao, Yueguang Chen, Dadong Zhang. Deceleration and acceleration capacities of heart rate associated with heart failure with high discriminating performance. Sci Rep. 2016; Mar 23; 6:23617. doi: 10.1038/srep23617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Cao Zou,1, Hongkai Dong, Fengyan Wang, Meiwen Gao, Xingmei Huang, Jianling Jin, Bingyuan Zhou, Xiangjun Yang. Heart acceleration and deceleration capacities associated with dilated cardiomyopathy. Eur J Clin Invest. 2016; 46 (4): 312–320.doi: 10.1111/eci.12594 [DOI] [PubMed] [Google Scholar]
- 5.Martin Duckheim, Martin Gaebler, Lars Mizera, Juergen Schreieck, Sven Poli, Ulf Ziemann, Meinrad Gawaz, Christine S. Meyer-Zuern, Christian Eick. Deceleration capacity for rapid risk stratification in patients suffering from acute ischemic stroke: A prospective exploratory pilot study. Medicine (Baltimore) 2021; 100:13(e25333). 10.1097/MD.0000000000025333 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Liyuan Yan, Jianling Jin, Xin Zhao, Xingmei Huang, Wei Zhu, Shili Jiang, Meiwen Gao, Jiamin Yuan. Heart rate acceleration and deceleration capacities associated with circadian blood pressure variation. Ann Noninvasive Electrocardiol 2020. Jul;25(4):e12748. 10.1111/anec.12748 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Yaping Wang, Mingyan Liu, Yaping Liu, Xiaowei Tang, Xiangming Tang. Assessment of heart rate deceleration capacity, heart rate deceleration runs, heart rate acceleration capacity, and lipoprotein-related phospholipase A2 as predictors in individuals with dementia. Front. Neurol. 2025; 15:1438736. doi: 10.3389/fneur. 2024.1438736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hongyun Liu, Ping Zhan, Jinlong Shi, Guojing Wang, Buqing Wang, Weidong Wang. A refined method of quantifying deceleration capacity index for heart rate variability analysis. BioMed Eng OnLine. 2018; 17,184. 10.1186/s12938-018-0618-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Alexander Steger, Petra Barthel, Alexander Müller, Ina-Maria Rückert-Eheberg, Birgit Linkohr, Julia Allescher, Melanie Maier, Alexander Hapfelmeier, Eimo Martens, Helene Hildegard Heidegger, Arne Michael Müller, Konstantinos D. Rizas, Stefan Kääb, Moritz F. Sinner, Daniel Sinnecker, Karl-Ludwig Laugwitz, Annette Peters, Georg Schmidt. Deceleration capacity derived from a five-minute electrocardiogram predicts mortality in the general population. Nature Scientific Reports (2024) 14:30566. 10.1038/s41598-024-83712-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Johnson TW, Dar IA, Donohue KL, Xu YY, Santiago E, Selioutski O, Marinescu MA, Maddox RK, Wu TT, Schifitto G, Gosev I, Choe R, Khan IR. Cerebral Blood Flow Hemispheric Asymmetry in Comatose Adults Receiving Extracorporeal Membrane Oxygenation. Front. Neurosci. 2022;16:858404. doi: 0.3389/fnins.2022.858404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lippman Neal, Kenneth M. Stein, Bruce B. Lerman. Comparison of methods for removal of ectopy in measurement of heart rate variability. Am. J. Physiol. 1994. Jul; 267 (1 Pt 2): H411-H418. doi: 10.1152/ajpheart.1994.267.1.H411. [DOI] [PubMed] [Google Scholar]
- 12.Mirja A. Salo, Heikki V. Huikuri, Tapio Seppanen. Ectopic Beats in Heart Rate Variability Analysis (2001): Effects of Editing on Time and Frequency Domain Measures. Ann Noninvasive Electrocardiol. 2001. Jan; 6(1):5-17. doi: 10.1111/j.1542-474x.2001.tb00080.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Mirja A. Peltola. Role of editing of R–R intervals in the analysis of heart rate variability. Front Physiol. 2012. May23; 3:148. doi:10:3389/fphys.2012.00148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.David J. Ewing, Christopher N. Martyn, Robert J.Young, Basil F. Clarke. The Value of Cardiovascular Autonomic Function Tests: 10 Years Experience in Diabetes. Diabetes Care, 1985. Sep-Oct; 8(5):491-8.doi: 10.2337/diacare.8.5.491. [DOI] [PubMed] [Google Scholar]
- 15.Vincenza Spallone, Dan Ziegler, Roy Freeman, Luciano Bernardi, Simona Frontoni, Rodica Pop-Busui, Martin Stevens, Peter Kempler, Jannik Hilsted, Solomon Tesfaye, Phillip Low, Paul Valensi on behalf of The Toronto Consensus Panel on Diabetic Neuropathy. Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management. Diabetes Metab Res Rev. 2011. Oct; 27(7):639-53. doi: 10.1002/dmrr.1239. [DOI] [PubMed] [Google Scholar]
- 16.Sanghamitra Pattnaik, Supriya Gupta, Upma Saxena, Monika Matlani, Raj Kapoor. Comparison of autonomic function tests and high-sensitivity C-reactivity protein in overweight patients of polycystic ovarian syndrome and overweight controls. Indian Journal of Physiology and Pharmacology. Volume 64, Issue 4,October-December 2020. doi: 10.25259/IJPP_72_2020. [DOI] [Google Scholar]
- 17.Spallone V, Bellavere F, Scionti L, Maule S, Quadri R, Bax G, Melga P, Viviani GL, Esposito K, Morganti R, Cortelli P; Diabetic Neuropathy Study Group of the Italian Society of Diabetology. Recommendations for the use of cardiovascular tests in diagnosing diabetic autonomic neuropathy. Nutr Metab Cardiovasc Dis. 2011. Jan; 21(1):69-78. doi: 10.1016/j.numecd.2010.07.005. [DOI] [PubMed] [Google Scholar]
- 18.Voss A, Schroeder R, Heitmann A, Peters A, Perz S. Short-Term Heart Rate Variability— Influence of Gender and Age in Healthy Subjects. PLoS One. 2015. Mar; 10(3): e0118308. doi: 10.1371/journal. pone.0118308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Campana L M, Owens R L, Clifford G D, Pittman S D, Malhotra. A Phase-rectified signal averaging as a sensitive index of autonomic changes with aging. J Appl Physiol (1985). 2010 Mar 25;108(6):1668–1673. doi: 10.1152/japplphysiol.00013.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Duca S T, Tudorancea Ionut, Haba M S C, Costache A D, Serban I L, Pavăl D R, Loghin C, Costache-Enache II. Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters. Medicina (Kaunas). 2024. Aug; 60(8), 1315. Doi: 10.3390/medicina60081315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Jijing Wang, Jinyi Xu, Yanyan Qiu, Ruike Yang, Wentao Wang, Chuanyu Gao. Cardiac deceleration capacity and acceleration capacity have diagnostic value in patients with vasovagal syncope regardless of age. Front. Cardiovasc. Med. 2024. Dec; 11:1495129. doi: 10.3389/fcvm.2024.1495129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Lihui Zheng, Wei Sun, Shangyu Liu, Erpeng Liang, Zhongpeng Du, Jingrui Guo, Lingmin Wu, Samuel J. Asirvatham, Yan Yao. The Diagnostic Value of Cardiac Deceleration Capacity in Vasovagal Syncope. Circ Arrhythm Electrophysiol. 2020. Dec;13(12):e008659. doi: 10.1161/CIRCEP.120.008659. [DOI] [PubMed] [Google Scholar]
- 23.Yang Y, Wang F, Zou C, Dong H, Huang X, Zhou B, Li X and Yang X. Male Patients With Dilated Cardiomyopathy Exhibiting a Higher Heart Rate Acceleration Capacity or a Lower Deceleration Capacity Are at Higher Risk of Cardiac Death. Front Physiol. 2018. Dec; 9:1774. doi: 10.3389/fphys.2018.01774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Petros Arsenos, George Manis, Polychronis Dilaveris, Dimitrios Tsiachris, Skevos Sideris, Ioannis Skiadas, Ilias Sotiropoulos, Ioannis Kalikazaros, Dimitrios Tousoulis, Konstantinos A. Gatzoulis. Decreased Deceleration Capacity of Heart Rate Detects Heart Failure Patients at Risk for Malignant Ventricular Arrhythmias. Hospital Chronicles. 2014; 9(4): 258–265. [Google Scholar]
- 25.Xiang Liu, Li Xiang, Guangming Tong. Predictive values of heart rate variability, deceleration and acceleration capacity of heart rate in post-infarction patients with LVEF ≥35%. Ann Noninvasive Electrocardiol. 2020. Nov; 25(6):e12771.doi: 10.1111/anec.12771. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yan-Hong Xu, Xing-De Wang, Jia-Jun Yang, Li Zhou, Yong-Chao Pan. Changes of deceleration and acceleration capacity of heart rate in patients with acute hemispheric ischemic stroke. Clin Interv Aging. 2016. Mar; 11: 293–298. 10.2147/CIA.S99542 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Arsenos P, Manis G, Gatzoulis KA, Dilaveris P, Gialernios T, Angelis A, Papadopoulos A, Venieri E, Trikas A, Tousoulis D. Deceleration Capacity of Heart Rate Predicts Arrhythmic and Total Mortality in Heart Failure Patients. Ann Noninvasive Electrocardiol. 2016. Sep; 21(5):50818. doi: 10.1111/anec.12343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Chessa M, Butera G, Lanza GA, et al. Role of heart rate variability in the early diagnosis of diabetic autonomic neuropathy in children. Herz. 2002;27(8):785–790. doi: 10.1007/s00059-002-2340-4. [DOI] [PubMed] [Google Scholar]