SUMMARYPurpose: Psychogenic nonepileptic seizures (PNES) superficially resemble epileptic seizures. Little is known about ictal autonomic nervous system (ANS) activity changes in epilepsy and PNES. This study compares ictal heart rate variability (HRV) parameters as a reflection of ANS tone in epileptic seizures and PNES, and explores differences between interictal and ictal ANS tone in both patient groups. Methods: Ictal HRV parameters were extracted from single-lead electrocardiography (ECG) data collected during video-electroencephalography (EEG) recordings of 26 patients with medically refractory temporal lobe epilepsy and 24 age-and sex-matched patients with PNES. One seizure per patient in a resting, wake, supine state was analyzed. Interictal ECG data were available for comparison from 14 patients in both groups. HRV parameters in time and frequency domains were analyzed (low frequency [LF], high frequency [HF], standard deviation of all consecutive normal R wave intervals [SDNN], square root of the mean of the sum of the squares of differences between adjacent normal R wave intervals [RMSSD]). CVI (cardiovagal index), CSI (cardiosympathetic index), and ApEn (approximate entropy) were calculated from Lorenz plots. Key Findings: There were significant differences between ictal HRV measures during epileptic and nonepileptic seizures in the time and frequency domains. CSI (p < 0.001) was higher in epileptic seizures. Time interval between two consecutive R waves in the ECG (RR interval) (p = 0.002), LF (p = 0.02), HF (p = 0.003), and RMSSD (p = 0.003) were significantly lower during epileptic seizures. Binary logistic regression yielded a significant model based on the differences in CSI classifying 88% of patients with epilepsy and 73% of patients with PNES correctly. The comparison between resting and ictal states in both seizure disorders revealed significant differences in RR interval (epilepsy p < 0.001, PNES p = 0.01), CSI (epilepsy p < 0.001, PNES p = 0.02), HF (epilepsy p = 0.002, PNES p = 0.03), and RMSSD (epilepsy p = 0.004, PNES p = 0.04). In patients with epilepsy there were also significant differences in ictal versus interictal mean values of ApEn (p = 0.03) and LF (p = 0.04). Although CSI was significantly higher, the other parameters were lower during the seizures. Stepwise binary regression in the 14 patients with epilepsy produced a significant model differentiating resting state from seizures in 100% of cases. The same statistical approach did not yield a significant model in the PNES group. Significance: Our results show greater ANS activation in epileptic seizures than in PNES. The biggest ictal HRV changes associated with epileptic seizures (CSI, HF, and RMSSD) reflect high sympathetic system activation and reduced vagal tone. The reduced ApEn also reflects a high sympathetic tone. The observed ictal alterations of HRV patterns may be a more specific marker of epileptic seizures than heart rate changes alone. These altered HRV patterns could be used to detect seizures and also to differ...

No abstract

OBJECTIVEHypoglycemia may exert proarrhythmogenic effects on the heart via sympathoadrenal stimulation and hypokalemia. Hypoglycemia-induced cardiac dysrhythmias are linked to the "dead-in-bed syndrome," a rare but devastating condition. We examined the effect of nocturnal and daytime clinical hypoglycemia on electrocardiogram (ECG) in young people with type 1 diabetes. RESEARCH DESIGN AND METHODSThirty-seven individuals with type 1 diabetes underwent 96 h of simultaneous ambulatory ECG and blinded continuous interstitial glucose monitoring (CGM) while symptomatic hypoglycemia was recorded. Frequency of arrhythmias, heart rate variability, and cardiac repolarization were measured during hypoglycemia and compared with time-matched euglycemia during night and day. RESULTSA total of 2,395 h of simultaneous ECG and CGM recordings were obtained; 159 h were designated hypoglycemia and 1,355 h euglycemia. A median duration of nocturnal hypoglycemia of 60 min (interquartile range 40-135) was longer than daytime hypoglycemia of 44 min (30-70) (P = 0.020). Only 24.1% of nocturnal and 51.0% of daytime episodes were symptomatic. Bradycardia was more frequent during nocturnal hypoglycemia compared with matched euglycemia (incident rate ratio [IRR] 6.44 [95% CI 6.26, 6.63], P < 0.001). During daytime hypoglycemia, bradycardia was less frequent (IRR 0.023 [95% CI 0.002, 0.26], P = 0.002) and atrial ectopics more frequent (IRR 2.29 [95% CI 1.19,4.39], P = 0.013). Prolonged QTc, T-peak to T-end interval duration, and decreased T-wave symmetry were detected during nocturnal and daytime hypoglycemia. CONCLUSIONSAsymptomatic hypoglycemia was common. We identified differences in arrhythmic risk and cardiac repolarization during nocturnal versus daytime hypoglycemia in young adults with type 1 diabetes. Our data provide further evidence that hypoglycemia is proarrhythmogenic.Hypoglycemia is an inevitable consequence of the current management of type 1 diabetes (1). Improved glycemic control is frequently accompanied by an increased risk of inducing iatrogenic hypoglycemia (2). Observational studies indicate that rates of severe hypoglycemia have generally not fallen despite the introduction of insulin

No abstract

No abstract

BackgroundAutonomic neuropathy is a common and serious complication of diabetes. Early detection is essential to enable appropriate interventional therapy and management. Dynamic pupillometry has been proposed as a simpler and more sensitive tool to detect subclinical autonomic dysfunction. The aim of this study was to investigate pupil responsiveness in diabetic subjects with and without cardiovascular autonomic neuropathy (CAN) using dynamic pupillometry in two sets of experiments.MethodsDuring the first experiment, one flash was administered and the pupil response was recorded for 3 s. In the second experiment, 25 flashes at 1-s interval were administered and the pupil response was recorded for 30 s. Several time and pupil-iris radius-related parameters were computed from the acquired data. A total of 24 diabetic subjects (16 without and 8 with CAN) and 16 healthy volunteers took part in the study.ResultsOur results show that diabetic subjects with and without CAN have sympathetic and parasympathetic dysfunction, evidenced by diminished amplitude reflexes and significant smaller pupil radius. It suggests that pupillary autonomic dysfunction occurs before a more generalized involvement of the autonomic nervous system, and this could be used to detect early autonomic dysfunction.ConclusionsDynamic pupillometry provides a simple, inexpensive, and noninvasive tool to screen high-risk diabetic patients for diabetic autonomic neuropathy.

Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

OBJECTIVEAlthough a clear link between diabetic peripheral neuropathy (DPN) and autonomic neuropathy is recognized, the relationship of autonomic neuropathy with subtypes of DPN is less clear. This study aimed to investigate the relationship of autonomic neuropathy with painless and painful DPN.RESEARCH DESIGN AND METHODSEighty subjects (20 healthy volunteers, 20 with no DPN, 20 with painful DPN, 20 with painless DPN) underwent detailed neurophysiological investigations (including conventional autonomic function tests [AFTs]) and spectral analysis of short-term heart rate variability (HRV), which assesses sympathovagal modulation of the heart rate. Various frequency-domain (including low frequency [LF], high frequency [HF], and total power [TP]) and time-domain (standard deviation of all normal-to-normal R-R intervals [SDNN] and root mean square of successive differences [RMSSD]) parameters were assessed.RESULTSHRV analysis revealed significant differences across the groups in LF, HF, TP, SDNN, and RMSSD (ANOVA P < 0.001). Subgroup analysis showed that compared with painless DPN, painful DPN had significantly lower HF (3.59 ± 1.08 [means ± SD] vs. 2.67 ± 1.56), TP (5.73 ± 1.28 vs. 4.79 ± 1.51), and SDNN (2.91 ± 0.65 vs. 1.62 ± 3.5), P < 0.05. No significant differences were seen between painless DPN and painful DPN using an AFT.CONCLUSIONSThis study shows that painful DPN is associated with significantly greater autonomic dysfunction than painless DPN. These changes are only detected using spectral analysis of HRV (a simple test based on a 5-min electrocardiogram recording), suggesting that it is a more sensitive tool to detect autonomic dysfunction, which is still under-detected in people with diabetes. The greater autonomic dysfunction seen in painful DPN may reflect more predominant small fiber involvement and adds to the growing evidence of its role in the pathophysiology of painful DPN.