Characterization of skin sympathetic nerve activity in patients with cardiomyopathy and ventricular arrhythmia

Autonomic neuronal modulations in cardiac arrhythmias: Current concepts and emerging therapies

The pathophysiology of atrial fibrillation and ventricular tachycardia that result in cardiac arrhythmias is related to the sustained complicated mechanisms of the autonomic nervous system. Atrial fibrillation is when the heart beats irregularly, and ventricular arrhythmias are rapid and inconsistent heart rhythms, which involves many factors including the autonomic nervous system. It's a complex topic that requires careful exploration. Cultivation of speculative knowledge on atrial fibrillation; the irregular rhythm of the heart and ventricular arrhythmias; rapid oscillating waves resulting from mistakenly inconsistent P waves, and the inclusion of an autonomic nervous system is an inconceivable approach toward clinical intricacies. Autonomic modulation, therefore, acquires new expansions and conceptions of appealing therapeutic intelligence to prevent cardiac arrhythmia. Notably, autonomic modulation uses the neural tissue's flexibility to cause remodeling and, hence, provide therapeutic effects. In addition, autonomic modulation techniques included stimulation of the vagus nerve and tragus, renal denervation, cardiac sympathetic denervation, and baroreceptor activation treatment. Strong preclinical evidence and early human studies support the annihilation of cardiac arrhythmias by sympathetic and parasympathetic systems to transmigrate the cardiac myocytes and myocardium as efficient determinants at the cellular and physiological levels. However, the goal of this study is to draw attention to these promising early pre-clinical and clinical arrhythmia treatment options that use autonomic modulation as a therapeutic modality to conquer the troublesome process of irregular heart movements. Additionally, we provide a summary of the numerous techniques for measuring autonomic tone such as heart rate oscillations and its association with cutaneous sympathetic nerve activity appear to be substitute indicators and predictors of the outcome of treatment.

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.

Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

OBJECTIVES

Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs.

METHODS

In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs.

RESULTS

Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs.

CONCLUSIONS

The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).

Etiologies, Mechanisms, Management, and Outcomes of Electrical Storm

Electrical storm (ES) is characterized by three or more discrete sustained ventricular tachyarrhythmia episodes occurring within a limited time frame (generally ≤ 24 h) or an incessant ventricular tachyarrhythmia lasting > 12 h. In patients with an implantable cardioverterdefibrillator (ICD), ES is defined as three or more appropriate device therapies, separated from each other by at least 5 min, which occur within a 24-h period. ES may constitute a medical emergency, depending on the number arrhythmic episodes, their duration, the type, and the cycle length of the ventricular arrhythmias, as well as the underlying ventricular function. This narrative review was facilitated by a search of MEDLINE to identify peer-reviewed clinical trials, randomized controlled trials, meta-analyses, and other clinically relevant studies. The search was limited to English-language reports published between 1999 and 2023. ES was searched using the terms mechanisms, genetics, channelopathies, management, pharmacological therapy, sedation, neuraxial modulation, cardiac sympathetic denervation, ICDs, and structural heart disease. Google and Google scholar as well as bibliographies of identified articles were reviewed for additional references. This manuscript examines the current strategies available to treat ES and compares pharmacological and invasive treatment strategies to diminish ES recurrence, morbidity, and mortality.

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.

The Mechanism of Cardiac Sympathetic Activity Assessment Methods: Current Knowledge

This review has summarized the methods currently available for cardiac sympathetic assessment in clinical or under research, with emphasis on the principles behind these methodologies. Heart rate variability (HRV) and other methods based on heart rate pattern analysis can reflect the dominance of sympathetic nerve to sinoatrial node function and indirectly show the average activity level of cardiac sympathetic nerve in a period of time. Sympathetic neurotransmitters play a key role of signal transduction after sympathetic nerve discharges. Plasma or local sympathetic neurotransmitter detection can mediately display sympathetic nerve activity. Given cardiac sympathetic nerve innervation, i.e., the distribution of stellate ganglion and its nerve fibers, stellate ganglion activity can be recorded either directly or subcutaneously, or through the surface of the skin using a neurophysiological approach. Stellate ganglion nerve activity (SGNA), subcutaneous nerve activity (SCNA), and skin sympathetic nerve activity (SKNA) can reflect immediate stellate ganglion discharge activity, i.e., cardiac sympathetic nerve activity. These cardiac sympathetic activity assessment methods are all based on the anatomy and physiology of the heart, especially the sympathetic innervation and the sympathetic regulation of the heart. Technological advances, discipline overlapping, and more understanding of the sympathetic innervation and sympathetic regulation of the heart will promote the development of cardiac sympathetic activity assessment methods.

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.

Skin Sympathetic Nerve Activity and the Short-Term QT Interval Variability in Patients With Electrical Storm

Background: Skin sympathetic nerve activity (SKNA) and QT interval variability are known to be associated with ventricular arrhythmias. However, the relationship between the two remains unclear.

Objective: The aim was to test the hypothesis that SKNA bursts are associated with greater short-term variability of the QT interval (STVQT) in patients with electrical storm (ES) or coronary heart disease without arrhythmias (CHD) than in healthy volunteers (HV).

Methods: We simultaneously recorded the ECG and SKNA during sinus rhythm in patients with ES (N = 10) and CHD (N = 8) and during cold-water pressor test in HV (N = 12). The QT and QTc intervals were manually marked and calculated within the ECG. The STVQT was calculated and compared to episodes of SKNA burst and non-bursting activity.

Results: The SKNA burst threshold for ES and HV was 1.06 ± 1.07 and 1.88 ± 1.09 μV, respectively (p = 0.011). During SKNA baseline and burst, the QT/QTc intervals and STVQT for ES and CHD were significantly higher than those of the HV. In all subjects, SKNA bursts were associated with an increased STVQT (from 6.43 ± 2.99 to 9.40 ± 5.12 ms, p = 0.002 for ES; from 9.48 ± 4.40 to 12.8 ± 5.26 ms, p = 0.016 for CHD; and from 3.81 ± 0.73 to 4.49 ± 1.24 ms, p = 0.016 for HV). The magnitude of increased STVQT in ES (3.33 ± 3.06 ms) and CHD (3.34 ± 2.34 ms) was both higher than that of the HV (0.68 ± 0.84 ms, p = 0.047 and p = 0.020).

Conclusion: Compared to non-bursting activity, SKNA bursts were associated with a larger increase in the QTc interval and STVQT in patients with heart disease than in HV.

Loss of nocturnal dipping pattern of skin sympathetic nerve activity during and following an extended-duration work shift in residents in training

BACKGROUND

Extended-duration work shifts (EDWSs) might affect the health of physician residents, causing autonomic alteration. Skin sympathetic nerve activity (SKNA) recorded by noninvasive neuro-electrocardiography (neuECG) is used to estimate cardiac sympathetic tone. In this study, we aim to evaluate the impact of EDWSs on nocturnal SKNA assessed in resident doctors.

METHODS

Twenty-four residents working EDWSs and 12 PhD students not working nightshift schedules were prospectively recruited. The neuECG was performed between 12 am and 6 am for 5 consecutive nights. SKNA was filtered from neuECG recorded signals. The questionnaires regarding work stress and sleep quality, blood pressure, and salivary alpha-amylase and cortisol levels were administered.

RESULTS

The hours of weekly working and sleep opportunities were similar between residents and students, while residents reported more work stress and worse sleep quality. In residents, SKNA at 6 am (SKNA_6am) was significantly higher than SKNA_2am during the precall night, revealing a dipping pattern. However, the SKNA dipping disappeared during the on-call night and prominently flattened during the first postcall night, the full recovery of which was delayed until the second postcall nights. The morning blood pressure and salivary alpha-amylase and cortisol levels were similar between the precall and postcall days. In contrast, SKNA in students exhibited a constant dipping profile for all recorded nights.

CONCLUSIONS

In healthy young adults, SKNA presents a dip night. The SKNA dip is impaired by working a nightshift, with a delayed recovery. The neuECG might serve as a useful tool to detect subclinical autonomic disturbances in shiftworkers.

Cardiomyopathies in China: A 2018-2019 state-of-the-art review

Cardiomyopathies are diseases of the cardiac muscle and are often characterized by ventricular dilation, hypertrophy, and cardiac arrhythmia. Patients with cardiomyopathies often experience sudden death and cardiac failure and require cardiac transplantation during the course of disease progression. Early diagnosis, differential diagnosis, and genetic consultation depend on imaging techniques, genetic testing, and new emerging diagnostic tools such as serum biomarkers. The molecular genetics of cardiomyopathies has been widely studied recently. The discovery of mechanisms underlying heterogeneity and overlapping of the phenotypes of cardiomyopathies has revealed the existence of disease modifiers, and this has led to the emergence of novel disease-modifying therapy. This 2018-2019 state-of-the-art review outlines the pathogenesis, diagnosis, and treatment of cardiomyopathies in China.

Management of ventricular electrical storm: a contemporary appraisal

Ventricular electrical storm (VES) is a clinical scenario characterized by the clustering of multiple episodes of sustained ventricular arrhythmias (VA) over a short duration. Patients with VES are prone to psychological disorders, heart failure decompensation, and increased mortality. Studies have shown that 10–28% of the patients with secondary prevention ICDs can sustain VES. The triad of a susceptible electrophysiologic substrate, triggers, and autonomic dysregulation govern the pathogenesis of VES. The rate of VA, underlying ventricular function, and the presence of implantable cardioverter-defibrillator (ICD) determine the clinical presentation. A multi-faceted approach is often required for management consisting of acute hemodynamic stabilization, ICD reprogramming when appropriate, antiarrhythmic drug therapy, and sedation. Some patients may be eligible for catheter ablation, and autonomic modulation with thoracic epidural anesthesia, stellate ganglion block, or cardiac sympathetic denervation. Hemodynamically unstable patients may benefit from the use of left ventricular assist devices, and extracorporeal membrane oxygenation. Special scenarios such as idiopathic ventricular fibrillation, Brugada syndrome, Long and short QT syndrome, early repolarization syndrome, catecholaminergic polymorphic ventricular tachycardia, arrhythmogenic right ventricular cardiomyopathy, and cardiac sarcoidosis have been described as well. VES is a cardiac emergency that requires swift intervention. It is associated with poor short and long-term outcomes. A structured team-based management approach is paramount for the safe and effective treatment of this sick cohort.