Skin sympathetic nerve activity as a biomarker of fitness

Analysis of sympathetic responses to cognitive stress and pain through skin sympathetic nerve activity and electrodermal activity

We explored the non-invasive evaluation of the sympathetic nervous system (SNS) by employing two distinct physiological signals: skin sympathetic nerve activity (SKNA), extracted from electrocardiogram (ECG) signals, and electrodermal activity (EDA), a well-studied marker in the context of the SNS assessment. Our investigation focused on cognitive stress and pain; two conditions closely associated with the SNS. We sought to determine if the information and dynamics of EDA could be derived from the novel SKNA signal. To this end, ECG and EDA signals were recorded simultaneously during three experiments aimed at sympathetic stimulation, Valsalva maneuver (VM), Stroop test, and thermal-grill pain test. We calculated the integral area under the rectified SKNA signal (iSKNA) and decomposed the EDA signal to its phasic component (EDAphasic). An average delay of more than 4.6 s was observed in the onset of EDAphasic bursts compared to their corresponding iSKNA bursts. After shifting the EDAphasic segments by the extent of this delay and smoothing the corresponding iSKNA bursts, our results revealed a strong average correlation coefficient of 0.85±0.14 between the iSKNA and EDAphasic bursts, indicating a noteworthy similarity between the two signals. We also reconstructed the EDA signals with time-varying sympathetic (TVSymp) and modified TVSymp (MTVSymp) methods. Then we extracted the following features from iSKNA, EDAphasic, TVSymp, and MTVSymp signals: peak amplitude, average amplitude (aSKNA), standard deviation (vSKNA), and the cumulative duration during which the signals had higher amplitudes than a specified threshold (HaSKNA). A strong average correlation of 0.89±0.18 was found between vSKNA and subjects' self-rated pain levels during the pain test. Our statistical analysis also included applying Linear Mixed-Effects Models to check if there were significant differences in features across baseline and different levels of SNS stimulation. We then assessed the discriminating power of the features using Area Under the Receiver Operating Characteristic Curve (AUROC) and Fisher's Ratio. Finally, using all the four EDA features, a multi-layer perceptron (MLP) classifier reached the classification accuracies 95.56%, 89.29%, and 67.88% for the VM, Stroop, and thermal-grill pain control and stimulation classes. On the other hand, the highest classification accuracies based on SKNA features were achieved using K-nearest neighbors (KNN) (98.89%), KNN (89.29%), and MLP (95.11%) classifiers for the same experiments. Our comparative analysis showed the feasibility of SKNA as a novel tool for assessing the SNS with accurate classification capability, with a faster onset of amplitude increase in response to SNS activity, compared to EDA.

Alterations of sympathetic dynamics after atrial fibrillation ablation by analysis sympathetic nerve activity provide prognostic value for recurrence and mechanistic insights into ablation

Background

Pulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation (AF) ablation. Success is associated with autonomic function modulation; however, the relationship between the changes after ablation is not fully understood. We aimed to investigate the effect of ablation on autonomic modulation by skin sympathetic nerve activity (SKNA) using conventional electrocardiogram (ECG) electrodes and to predict the treatment success.

Methods

We enrolled 79 patients. We recorded neuECG for 10 min at 10 kHz before and after ablation. The NeuECG was bandpass-filtered (500–1,000 Hz) and integrated at intervals of 100 ms (iSKNA). iSKNA was averaged over different time windows (1-, 5-,10-s; aSKNAs), and burst analyses were derived from aSKNAs to quantify the dynamics of sympathetic activities. AF recurrence after 3 months was defined as the study endpoint.

Results

Sixteen patients experienced AF recurrence after the ablation. For burst analysis of 1-s aSKNA, the recurrence group had a higher bursting frequency than the non-recurrence group (0.074 ± 0.055 vs. 0.109 ± 0.067; p < 0.05) before ablation. The differences between pre- and post-ablation of firing duration longer than 2 s were more in the non-recurrence group (2.75 ± 6.41 vs. −1.41 ± 5.14; p < 0.05), while no significant changes were observed in the percentage of duration longer than 10 s using 5-s aSKNA. In addition, decreases in differences in firing frequency and percentage of both overall firing duration and longer firing duration (> 2 s) between pre- and post-ablation were independently associated with AF recurrence and more area under receiver operating characteristics (ROC) curve in combination with CHADS2 score (0.833).

Conclusion

We demonstrated the applicability of neuECG for determining sympathetic modulation during AF ablation. Decreasing sympathetic activity is the key to successful ablation.

Autonomic nervous activity analysis based on visibility graph complex networks and skin sympathetic nerve activity

Background: Autonomic nerve system (ANS) plays an important role in regulating cardiovascular function and cerebrovascular function. Traditional heart rate variation (HRV) and emerging skin sympathetic nerve activity (SKNA) analyses from ultra-short-time (UST) data cannot fully reveal neural activity, thereby quantitatively reflect ANS intensity.

Methods: Electrocardiogram and SKNA from sixteen patients (seven cerebral hemorrhage (CH) patients and nine control group (CO) patients) were recorded using a portable device. Ten derived HRV (mean, standard deviation and root mean square difference of sinus RR intervals (NNmean, SDNN and RMSSD), ultra-low frequency (<0.003 Hz, uLF), very low frequency ([0.003 Hz, 0.04 Hz), vLF), low frequency ([0.04 Hz, 0.15 Hz), LF) and high frequency power ([0.15 Hz, 0.4 Hz), HF), ratio of LF to HF (LF/HF), the standard deviation of instantaneous beat-to-beat R-R interval variability (SD1), and approximate entropy (ApEn)) and ten visibility graph (VG) features (diameter (Dia), average node degree (aND), average shortest-path length (aSPL), clustering coefficient (CC), average closeness centrality (aCC), transitivity (Trans), average degree centrality (aDC), link density (LD), sMetric (sM) and graph energy (GE) of the constructed complex network) were compared on 5-min and UST segments to verify their validity and robustness in discriminating CH and CO under different data lengths. Besides, their potential for quantifying ANS-Load were also investigated.

Results: The validation results of HRV and VG features in discriminating CH from CO showed that VG features were more clearly distinguishable between the two groups than HRV features. For effectiveness evaluation of analyzing ANS on UST segment, the NNmean, SDNN, RMSSD, LF, HF and LF/HF in HRV features and the CC, Trans, Dia and GE of VG features remained stable in both activated and inactivated segments across all data lengths. The capability of HRV and VG features in quantifying ANS-Load were evaluated and compared under different ANS-Load, the results showed that most HRV features (SDNN, LFHF, RMSSD, vLF, LF and HF) and almost all VG features were correlated to sympathetic nerve activity intensity.

Conclusions: The proposed autonomic nervous activity analysis method based on VG and SKNA offers a new insight into ANS assessment in UST segments and ANS-Load quantification.

Skin Sympathetic Nerve Activity and Ventricular Arrhythmias in Acute Coronary Syndrome

BACKGROUND

Acute coronary syndrome (ACS) is major cause of ventricular arrhythmias (VA) and sudden death. neuECG is a non-invasive method to simultaneously measure skin sympathetic nerve activity (SKNA) and electrocardiogram (ECG).

OBJECTIVE

To test the hypotheses that (1) ACS increases the average SKNA (aSKNA), (2) the magnitude of aSKNA elevation is associated with VA during ACS and (3) there is a gender difference of aSKNA in patients without and with ACS.

METHODS

We prospectively studied 128 ACS and 165 control participants. The neuECG was recorded with electrocardiogram (ECG) Lead I configuration at baseline, during mental math stress and during recovery (5-min each). All recordings were done in the morning.

RESULTS

In control group, women have higher aSKNA (μV) than men at baseline (0.82±0.25 vs 0.73±0.20, p=0.009) but not during mental stress (1.21±0.36 vs 1.16±0.36, p=0.394), suggesting women had lower sympathetic reserve. In comparison, ACS is associated with equally elevated aSKNA (μV) in women vs men at baseline (1.14±0.33 vs 1.04±0.35, p=0.531), during mental stress (1.46±0.32 vs 1.33 ±0.37, p=0.113) and during recovery (1.30±0.33 1.11±0.30, p=0.075). After adjusting for age and gender, the adjusted odds ratio for VA including ventricular tachycardia and fibrillation is 1.23 (95% confidence interval 1.05-1.44) for each 0.1 μV elevation of aSKNA. The aSKNA is positively correlated with plasma norepinephrine level.

CONCLUSIONS

ACS is associated with elevated aSKNA and the magnitude of aSKNA elevation is associated with occurrences of VA. Women have higher aSKNA and lower SKNA reserve than men in control but not in ACS patients.