Gentili Claudio, Valenza Gaetano, Nardelli Mimma, Lanatà Antonio, Bertschy Gilles, Weiner Luisa, Mauri Mauro, Scilingo Enzo Pasquale, Pietrini Pietro
Department of General Psychology, University of Padua, Via Venezia 8, 35139 Padua, Italy.
Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy.
J Affect Disord. 2017 Feb;209:30-38. doi: 10.1016/j.jad.2016.11.008. Epub 2016 Nov 12.
Recent research indicates that Heart Rate Variability (HRV) is affected in Bipolar Disorders (BD) patients. To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course, we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state.
Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project. We extracted time-domain, frequency-domain and non-linear HRV-features and trained a Support Vector Machine (SVM) to classify HRV-features according to mood state. To evaluate the influence of adjacent mood states, we trained SVM with different HRV-feature sets: 1) belonging to each mood state considered alone; 2) belonging to each mood state and normalized using information from the preceding mood state; 3) belonging to each mood state and normalized using information from the preceding and subsequent mood states; 4) belonging to each mood state and normalized using information from two randomly chosen states.
SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered.
Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones. Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients.
近期研究表明，双相情感障碍（BD）患者的心率变异性（HRV）会受到影响。为了确定这些改变仅仅是当前情绪状态的一种表现，还是包含有关BD病程的纵向信息，我们研究了在目标情绪状态下，相邻时间状态对所测量的HRV特征的潜在影响。
通过使用PSYCHE项目中开发的可穿戴监测系统，对8名BD患者进行了HRV的纵向评估。我们提取了时域、频域和非线性HRV特征，并训练了支持向量机（SVM），以便根据情绪状态对HRV特征进行分类。为了评估相邻情绪状态的影响，我们使用不同的HRV特征集训练SVM：1）仅属于每个单独考虑的情绪状态；2）属于每个情绪状态，并使用来自前一个情绪状态的信息进行归一化；3）属于每个情绪状态，并使用来自前一个和后一个情绪状态的信息进行归一化；4）属于每个情绪状态，并使用来自两个随机选择状态的信息进行归一化。
当考虑来自前一个和后一个情绪状态的HRV特征时，目标状态内的SVM分类准确率显著更高。
尽管我们的结果是初步的且需要重复验证，但首次表明BD中的心理生理状态包含与后续状态相关的信息。这一特征可用于改善临床管理，并开发算法来预测个体患者的临床病程和情绪转换。
