Aug 2026· Asian Journal of Advanced Research and Reports· 0 citations
TL;DR
Compared with young healthy controls, the older and diabetic groups showed lower mean values for SDNN, RMSSD, NN50, pNN50, TINN, total power, SD1, and SD2.
Abstract
Background: Heart rate variability (HRV) reflects autonomic modulation of cardiac activity and may vary with age and diabetes mellitus. This study examined age- and diabetes-associated patterns in HRV using short-term electrocardiogram (ECG) recordings.
Methods: Six-minute lead-II ECG signals were acquired at 256 Hz using a custom-made three-lead ECG system from 20 young healthy controls, 20 older healthy controls, and 18 diabetic patients. The signals were preprocessed, RR intervals were detected using a MATLAB-based algorithm, and HRV was analysed in Kubios using time-domain, frequency-domain, and nonlinear measures.
Results: Compared with young healthy controls, the older and diabetic groups showed lower mean values for SDNN, RMSSD, NN50, pNN50, TINN, total power, SD1, and SD2. Mean total power was 5854.80 ms² in young healthy controls, 1620.80 ms² in older healthy controls, and 1593.94 ms² in diabetic patients. Mean SD1 values were 52.92, 12.56, and 11.13 ms, respectively, while mean SD2 values were 98.66, 47.94, and 51.41 ms. The diabetic and older healthy groups displayed broadly similar descriptive HRV patterns. Conclusion: The findings indicate lower cardiac autonomic variability in the older and diabetic groups under the study conditions. Larger, matched studies with standardised recording conditions and inferential analyses are required.
Background Heart rate variability (HRV) is a critical biomarker for assessing autonomic dysfunction, particularly in elderly and type 2 diabetes mellitus (T2DM) populations. However, traditional HRV indices, derived from R-R intervals (RRIs), often fail to detect subtle autonomic dysfunction in complex clinical scenarios. This study proposes a novel Direct TRI Correction (DTRC) sequence to optimize HRV assessment by directly extracting TR intervals (DTRI) and applying a single-step correction. The aim is to enhance accuracy in evaluating HRV, particularly in elderly and T2DM cohorts, where early detection is critical for preventing cardiovascular complications. Methods The study enrolled 124 participants (60 T2DM patients and 64 healthy controls) stratified into three groups based on glycemic control. HRV indices were derived from traditional RRI sequences, two-step corrected TRC sequences, and the novel single-step corrected DTRC sequences, respectively. Statistical analyses were performed on the HRV indices of three sequences to evaluate the comparative performance of DTRC sequences. Results DTRC-based HRV indices demonstrated superior sensitivity and discriminative capability compared to both RRI-based and TRC-based HRV indices. Significant improvements were observed in RMSSD (p = 0.007), pNN50 (p = 0.043), SDSD (p = 0.007), LHR (p = 0.016), SSR (p = 0.010), BEI (p = 0.002), and MSELS (p = 0.009). Visualization analysis and ROC curves confirmed clearer inter-group separation, particularly for T2DM patients with poor glycemic control. Conclusions The DTRC sequence significantly enhances HRV assessment accuracy, offering a reliable tool for early detection of autonomic dysfunction in high-risk populations. Its simplified correction process and improved sensitivity hold promise for clinical diagnostics and wearable health monitoring applications.
Shanglin Yang, Hongbin Zhou, Xuwei Liao et al.· Frontiers in Cardiovascular...· 0 citations
Impaired autonomic function characterized by reduced parasympathetic modulation may occur in Parkinson’s disease according to time domain analysis of HRV.
Shamima Sultana, Mefhtahul Jannat, Esha Chowdhury et al.· Journal of Bangladesh Societ...· 0 citations
Background Ventricular arrhythmia (VA) is a common complication in patients with coronary heart disease (CHD), and heart rate variability (HRV) is considered a potential marker for VA risk. Aims This study aimed to analyse the correlation between HRV and VA in patients with CHD. Methods From January 2020 to July 2024, patients with CHD who were treated at our hospital were divided into two groups, based on the presence of VA: theVA group (VA present) and the control group (no VA). Heart rate variability indices were measured using 24-hour dynamic electrocardiogram monitoring. The HRV parameters included the standard deviation of sinus R-R (N-N) interval (SDNN), the standard deviation of sinus R-R (N-N) mean value every 5 min (SDANN), the root mean square of adjacent RR interval difference (rMSSD), and the percentage of the number of adjacent R-R interval differences >50 ms in the total number of sinus beats (PNN50). Clinical data were compared between groups, and linear regression was used to explore the relationship between HRV and VA. Results The HRV indices SDNN, SDANN, rMSSD, and PNN50 were significantly lower in the VA group than in the non-VA group (P < 0.05). Linear regression analysis showed that SDANN, rMSSD and PNN50 were significantly associated with VA occurrence (P < 0.05). The linear equation derived was Y = 1.976 − 0.006 × X2 − 0.009 × X3 − 0.007 × X4. Conclusion The HRV indices, particularly SDANN, rMSSD, and PNN50, were significantly associated with the occurrence of VA in patients with CHD and may serve as potential indicators for VA risk stratification.
Respiratory frequency is a critical biomarker in sleep medicine and circadian biology. We investigated whether the high-frequency (HF) component of heart-rate variability (HRV)—which reflects respiratory sinus arrhythmia (RSA)—can serve as a non-invasive proxy for breathing rate estimation from ECG or PPG. We employed a two-stage validation design: (1) a physiologically calibrated simulation study (N = 30 per condition, five conditions, 180 s recordings) for controlled error characterization using the PhysioNet ECG-ID Database (Electrocardiogram Identification Database, DOI: 10.13026/C2XW26) as the processing pipeline reference; and (2) pilot real-data validation in N = 10 older adult participants (mean age 71.3 years) with simultaneous ECG and thermistor respiratory reference measured using the East Medic Biotope Mini (1000 Hz). Results: Under controlled resting conditions, simulation yielded MAE = 0.46 bpm (SNR = 4.8). An empirical error formula MAE = 2.017 × SNR^(−1.187) (R2 = 0.71) was derived. In the real-data validation, 4/10 participants achieved MAE ≤ 2.0 bpm; the remaining 6/10 showed errors of 6–17 bpm attributable to non-respiratory HF oscillations, harmonic confusion, and breathing rate variability. The HF-peak method is reliable when SNR is high and breathing is regular but requires additional quality criteria beyond SNR alone in older adult populations where non-respiratory HF oscillations may confound spectral peak detection.
Emi Yuda, Naoya Morikawa, J. Hayano· Clocks & Sleep· 0 citations
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.
Stigi Joseph, A. Sudhakar, JK Mukkadan· Nigerian Medical Journal· 0 citations
AIMS
We aimed to compare heart rate variability (HRV) between adolescents with type 1 diabetes and healthy age- and sex-matched control participants. We were also interested in how HRV may change during puberty.
RESEARCH DESIGN AND METHODS
Eighty-five adolescents with type 1 diabetes (mean diabetes duration 7.0 ± 3.6 years) and 103 healthy age- and sex-matched youths consented to this cross-sectional study. We derived HRV data for time domain and power spectra analyses from Holter recordings (mean duration 17.8 h) and analyzed associated risk factors for HRV deterioration during total recording time and nighttime separately.
RESULTS
Total low frequency (LF) and very low frequency (VLF) domains (ms2) were significantly lower in children with type 1 diabetes than in healthy controls (LF, P = 0.049 and VLF, P = 0.005, respectively), whereas no differences were found in the measurements taken at night. The most significant predictors for untoward changes in power spectra were skinfold sum and lipids. Mean heart rate (beats/min), or any of the time domain variables did not differ significantly between study groups. Compared with those in the lowest HbA1c tertile, adolescents with type 1 diabetes in the highest HbA1c tertile had significantly lower LF and VLF power spectra results.
CONCLUSIONS
Adolescents with type 1 diabetes have depressed HRV from low to very low frequencies. Deterioration of autonomic nervous system function in adolescents with type 1 diabetes, measured with HRV power spectra is mainly associated with metabolic factors, although it is not possible to establish causal links between these variables.
Minna Koivikko, Katri Helttula, Mikko P Tulppo et al.· Acta Diabetologica· 0 citations
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