Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

IntroductionThis study aims to (i)evaluate the diagnostic sensitivity, specificity and predictive values of seizure-related heart rate (HR) in differentiating epileptic seizures(ES) from psychogenic non-epileptic seizures(PNES), (ii)define the most useful point of HR measurement: pre-ictal, ictal-onset, maximal-ictal or post-ictal, and (iii)define the HR cut-off points to differentiate ES from PNES.MethodsAll video EEG(VEEG) at Monash Health from May 2009 to November 2015 were retrospectively reviewed. Baseline(during wakefulness), one-minute pre-ictal, ictal-onset, maximal-ictal and one-minute post-ictal HR were measured for each ES and PNES event. Events less than ten seconds or with uninterpretable ECG due to artefacts were excluded. ROC curve analysis was performed to study the diagnostic accuracy reflected by area under the curve(AUC). The AUC was interpreted as follows; ≤0.5, differentiation of PNES from ES no better than chance; 0.80–0.89, good differentiation; and 0.9–1, excellent differentiation.ResultsVEEG of 341 ES and 265 PNES from 130 patients were analysed. The AUC for pre-ictal, ictal-onset, maximal-ictal and post-ictal HR were found to have poor differentiation between ES and PNES. Comparing PNES and bilateral tonic-clonic ES, AUC for absolute maximal-ictal HR was 0.84(CI 0.73–0.95) and for absolute post-ictal HR was 0.90(CI 0.81–1.00). Using Youden’s index, to diagnose tonic-clonic ES, the optimal cut-off point for absolute maximal-ictal HR was 114bpm (sensitivity 84%;specificity 82%;PPV 26.7%,NPV 98.5%) and for absolute post-ictal HR was 90bpm(sensitivity 91%;specificity 82%;PPV 30.3%;NPV 99.1%).ConclusionsThese findings suggest that seizure-related HR increase is useful in differentiating bilateral tonic-clonic ES from PNES. Based on the AUC, the best diagnostic measureme.

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Linear and nonlinear analyses of heart rate variability in obstructive sleep apnea with or without white matter change

Sleep Medicine ◽

10.1016/j.sleep.2015.02.221 ◽

2015 ◽

Vol 16 ◽

pp. S85-S86

Author(s):

K. Choi ◽

J. Kim ◽

A. Jung ◽

H. Lee

Keyword(s):

Heart Rate ◽

Obstructive Sleep Apnea ◽

Heart Rate Variability ◽

Sleep Apnea ◽

White Matter ◽

White Matter Change ◽

Nonlinear Analyses ◽

Obstructive Sleep ◽

Linear And Nonlinear

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Comparison of Linear and Nonlinear HRV Dynamics Across Exercise Intensities After Menopause

Journal of Aging and Physical Activity ◽

10.1123/japa.2018-0407 ◽

2020 ◽

Vol 28 (1) ◽

pp. 149-154

Author(s):

Julia C. Orri ◽

Elizabeth M. Hughes ◽

Deepa G. Mistry ◽

Antone Scala

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Exercise Training ◽

Postmenopausal Women ◽

Maximal Exercise ◽

Exercise Recovery ◽

Active Recovery ◽

Vigorous Exercise ◽

Main Effect ◽

Linear And Nonlinear

The authors compared the linear and nonlinear heart rate variability dynamics from rest through maximal exercise in postmenopausal women who trained at either moderate or high intensities. The outcome variables included the RR triangular index, TINN, SD1, SD2, SD1/SD2, DFA α1, DFA α2, and α1/α2. Maximal exercise reduced SD1, SD2, DFA α1, DFA α2, α1/α2, RRTri, and TINN in both groups and increased SD1/SD2 (p < .05). Two minutes of active recovery produced significant increases in SD1, SD2, DFA α1, and TINN, compared with exercise in both groups (p < .0001). There was also a significant main effect between groups for RRTri during exercise recovery, with the moderate group achieving higher levels (p < .04). The authors have shown that both moderate and vigorous exercise training can lead to a healthy response to maximal exercise and recovery, with the moderate group having a slightly improved recovery in the triangular index.

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