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

Yantao Xing; Hongyi Cheng; Chenxi Yang; Zhijun Xiao; Chang Yan; FeiFei Chen; Jiayi Li; Yike Zhang; Chang Cui; Jianqing Li; Chengyu Liu

doi:10.1016/j.compbiomed.2023.107397

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

Comput Biol Med. 2023 Sep 30:166:107397. doi: 10.1016/j.compbiomed.2023.107397. Online ahead of print.

Authors

Yantao Xing¹, Hongyi Cheng², Chenxi Yang¹, Zhijun Xiao¹, Chang Yan¹, FeiFei Chen¹, Jiayi Li¹, Yike Zhang², Chang Cui², Jianqing Li³, Chengyu Liu⁴

Affiliations

¹ State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China.
² Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210096, China.
³ State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China. Electronic address: ljq@seu.edu.cn.
⁴ State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China. Electronic address: chengyu@seu.edu.cn.

PMID: 37804780
DOI: 10.1016/j.compbiomed.2023.107397

Abstract

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.