Neurally mediated syncope (NMS) is the most common type of syncope, and head up tilt test (HUTT) is, so far, the most appropriate tool to identify NMS. In this work, an effort to predict the NMS before performing the HUTT is attempted. To achieve this, the heart rate variability (HRV) at rest and during the first minutes of tilting position during HUTT was analyzed using both time and frequency domains. Various features from HRV regularity and complexity, along with wavelet higher-order spectrum (WHOS) analysis in low-frequency (LF) and high-frequency (HF) bands were examined. The experimental results from 26 patients with history of NMS have shown that at rest, a time domain entropy measure and WHOS-based features in LF band exhibit significant differences between positive and negative HUTT as well as among 10 healthy subjects and NMS patients. The best performance of multilayer perceptron neural network (MPNN) was achieved by using an input vector consisted of WHOS-based HRV features in the LF zone and systolic blood pressure from the resting period, yielding an accuracy of 89.7%, assessed by 5-fold cross-validation. The promising results presented here pave the way for an early prediction of the HUTT outcome from resting state, contributing to the identification of patients at higher risk NMS.

神经介导的晕厥 (NMS) 是最常见的晕厥类型，而抬头倾斜试验 (HUTT) 是迄今为止识别 NMS 的最合适工具。在这项工作中，尝试在执行 HUTT 之前预测 NMS。为了实现这一点，使用时域和频域分析了在 HUTT 期间静息和倾斜位置的第一分钟内的心率变异性 (HRV)。检查了来自 HRV 规律性和复杂性的各种特征，以及低频 (LF) 和高频 (HF) 频带中的小波高阶频谱 (WHOS) 分析。来自 26 名有 NMS 病史的患者的实验结果表明，在休息时，LF 波段中的时域熵测量和基于 WHOS 的特征在 HUTT 正负之间以及 10 名健康受试者和 NMS 患者之间表现出显着差异。多层感知器神经网络 (MPNN) 的最佳性能是通过使用由 LF 区中基于 WHOS 的 HRV 特征和静息期收缩压组成的输入向量实现的，精度为 89.7%，通过 5 倍评估交叉验证。这里呈现的有希望的结果为从静息状态早期预测 HUTT 结果铺平了道路，有助于识别高危 NMS 患者。