State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China.
Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210096, China.
Comput Biol Med. 2023 Nov;166:107397. doi: 10.1016/j.compbiomed.2023.107397. Epub 2023 Sep 30.
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.
脑出血（ICH）的分类和预后预测对于提高患者的生存率至关重要。ICH 患者常出现早期或迟发性神经功能恶化，这可能导致自主神经系统（ANS）发生变化。因此，我们提出了一种基于皮肤交感神经活动（SKNA）信号的 ICH 分类和预后预测新框架。在之前的论文中介绍了一种定制的测量设备来收集数据。本研究共招募了 117 名受试者（50 名健康对照和 67 名 ICH 患者），以获得他们的 5 分钟心电图（ECG）和 SKNA 信号。我们提取了信号的时域、频域和非线性特征，并分析了它们在健康对照组和 ICH 患者之间的差异。随后，我们基于极端梯度提升（XGBoost）建立了 ICH 分类和预后评估模型。此外，心率变异性（HRV）作为 ANS 评估方法也被纳入本研究作为比较方法。结果表明，健康对照组和 ICH 患者之间 SKNA 信号的大多数特征存在显著差异。预后良好的 ICH 患者的 SKNA 信号变化率和复杂度高于预后不良的患者。此外，基于 SKNA 信号的 ICH 分类和预后预测模型的准确率分别超过 91%和 83%。基于 SKNA 信号的 ICH 分类和预后预测被证明是本研究中一种可行的方法。此外，变化率和复杂度等特征，如熵度量，可以用于描述不同组 SKNA 信号的差异。该方法有可能为 ICH 患者的快速分类和预后预测提供新的工具。
