Comparison of heart rate variability parameters during complex partial seizures and psychogenic nonepileptic seizures
Ponnusamy A et al. – The results show greater ANS activation in epileptic seizures than in Psychogenic nonepileptic seizures (PNES). The biggest ictal heart rate variability (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 differentiate epileptic seizures from PNES. Larger studies are justified with intergroup and intragroup comparisons between ictal and resting states.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.
- 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.