Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG

Effects of Respiratory Rate on Heart Rate Variability in Neurologic Outpatients with Epilepsies or Migraine: A Preliminary Study

BACKGROUND

Variation of spontaneous respiratory rates and influences of spontaneous and paced breathing rates on heart rate variability (HRV) were assessed in patients with epilepsy or migraine, and HRV parameters were compared between the groups.

MATERIALS AND METHODS

Thirty neurologic outpatients, 16 diagnosed with epilepsies and 14 with migraine, were included. Autonomic testing consisted of short-term HRV, the deep breathing test (DBT), and measurement of HRV at systematically changed breathing rates (paced breathing, 5-18 breaths per minute, bpm).

RESULTS

Spontaneous respiratory rate during short-term HRV varied from 9 to 23 bpm in the epileptic group and from 5 to 21 bpm in migraine patients and was significantly and negatively correlated with SD of all normal RR intervals (SDNN) and total power (TP) in epileptic patients but not in migraine patients. Paced breathing rate had a significant effect on all HRV parameters assessed in both groups. HRV (SD1, SDNN, TP) and DBT (E-I, SD1, SDNN) parameters were significantly lower in the epileptic group. Group differences were significantly greater during slow compared to fast breathing.

CONCLUSIONS

An important and new finding is the wide variation of spontaneous respiratory rate in both groups, along with the significant negative correlation with the assessed HRV parameters. The reduction of HRV during slow breathing in epileptic patients may indicate a diminished cardiorespiratory coupling caused by a probable loss of sensitivity within the cardiovagal brainstem circuitry.

Ictal autonomic changes as a tool for seizure detection: a systematic review

Purpose

Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters.

Methods

The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms.

Results

Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h).

Conclusions

The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection.

Electronic supplementary material

The online version of this article (10.1007/s10286-018-0568-1) contains supplementary material, which is available to authorized users.