Key result
A machine learning model based on skin sympathetic nervous activity signals achieved over 91% accuracy for intracerebral hemorrhage classification and over 83% accuracy for outcome prediction.
Why the study?
Intracerebral hemorrhage classification and outcome prediction are critical to improve survival, and neurological deterioration may alter autonomic nervous system activity.
Does skin sympathetic nervous activity (SKNA) signal analysis accurately classify intracerebral hemorrhage and predict patient outcomes?
Observational (n=117)
No
Does skin sympathetic nervous activity (SKNA) signal analysis accurately classify intracerebral hemorrhage and predict patient outcomes?
Skin sympathetic nervous activity (SKNA) analysis combined with machine learning provides a feasible, noninvasive tool for the rapid classification and outcome prediction of intracerebral hemorrhage.
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 five-minute 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, HRV as an autonomic nerve 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.
No takes yet. Share an insight, caveat, or question.
Xing et al. (2023) conducted an observational in Intracerebral Hemorrhage (ICH) (n=117). Skin sympathetic nervous activity (SKNA) measurement vs. Healthy controls was evaluated on Accuracy of ICH classification and outcome prediction. A machine learning model based on skin sympathetic nervous activity signals achieved over 91% accuracy for intracerebral hemorrhage classification and over 83% accuracy for outcome prediction.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: