An Artifact-Resistant Feature SKNAER for Quantifying the Burst of Skin Sympathetic Nerve Activity Signal

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.

Evaluation of skin sympathetic nervous activity for classification of intracerebral hemorrhage and outcome prediction

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals. A customized measurement device presented in our previous papers was used to collect data. 117 subjects (50 healthy control subjects and 67 ICH patients) were recruited for this study to obtain their 5-min electrocardiogram (ECG) and SKNA signals. We extracted the signal's time-domain, frequency-domain, and nonlinear features and analyzed their differences between healthy control subjects and ICH patients. Subsequently, we established the ICH classification and outcome evaluation model based on the eXtreme Gradient Boosting (XGBoost). In addition, heart rate variability (HRV) as an ANS assessment method was also included as a comparison method in this study. The results showed significant differences in most features of the SKNA signal between healthy control subjects and ICH patients. The ICH patients with good outcomes have a higher change rate and complexity of SKNA signal than those with bad outcomes. In addition, the accuracy of the model for ICH classification and outcome prediction based on the SKNA signal was more than 91% and 83%, respectively. The ICH classification and outcome prediction based on the SKNA signal proved to be a feasible method in this study. Furthermore, the features of change rate and complexity, such as entropy measures, can be used to characterize the difference in SKNA signals of different groups. The method can potentially provide a new tool for rapid classification and outcome prediction of ICH patients. Index Terms-intracerebral hemorrhage (ICH), skin sympathetic nervous activity (SKNA), classification, outcome prediction, cardiovascular and cerebrovascular diseases.

Single-lead ECG based autonomic nervous system assessment for meditation monitoring

We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.

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.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

Intelligent Biosignal Processing in Wearable and Implantable Sensors

Wearable technology including sensors, sensor networks, and the associated devices have opened up space in a variety of applications [...].