Valenza G, Citi L, Gentili C, Lanatá A, Scilingo E P, Barbieri R
Gaetano Valenza, Ph.D., Department of Information Engineering, Research Centre "E. Piaggio", Faculty of Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy.
Methods Inf Med. 2014;53(4):296-302. doi: 10.3414/ME13-02-0036. Epub 2014 Jun 27.
This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems".
The goal of this work is to apply a computational methodology able to characterize mood states in bipolar patients through instantaneous analysis of heartbeat dynamics.
A Point-Process-based Nonlinear Autoregressive Integrative (NARI) model is applied to analyze data collected from five bipolar patients (two males and three females, age 42.4 ± 10.5 range 32 -56) undergoing a dedicated affective elicitation protocol using images from the International Affective Picture System (IAPS) and Thematic Apperception Test (TAT). The study was designed within the European project PSYCHE (Personalised monitoring SYstems for Care in mental HEalth).
RESULTS demonstrate that the inclusion of instantaneous higher order spectral (HOS) features estimated from the NARI nonlinear assessment significantly improves the accuracy in successfully recognizing specific mood states such as euthymia and depression with respect to results using only linear indices. In particular, a specificity of 74.44% using the instantaneous linear features set, and 99.56% using also the nonlinear feature set were achieved. Moreover, IAPS emotional elicitation resulted in a more discriminant procedure with respect to the TAT elicitation protocol.
A significant pattern of instantaneous heartbeat features was found in depressive and euthymic states despite the inter-subject variability. The presented point-process Heart Rate Variability (HRV) nonlinear methodology provides a promising application in the field of mood assessment in bipolar patients.
本文是《医学信息方法》关于“生物信号解读：心血管和呼吸系统研究的先进方法”重点主题的一部分。
本研究的目的是应用一种计算方法，通过对心跳动态的即时分析来表征双相情感障碍患者的情绪状态。
应用基于点过程的非线性自回归积分（NARI）模型，分析从五名双相情感障碍患者（两名男性和三名女性，年龄42.4±10.5，范围32 - 56岁）收集的数据，这些患者使用国际情感图片系统（IAPS）和主题统觉测验（TAT）的图像进行专门的情感诱发方案。该研究是在欧洲项目PSYCHE（心理健康个性化监测系统）中设计的。
结果表明，与仅使用线性指标的结果相比，纳入从NARI非线性评估中估计的即时高阶谱（HOS）特征显著提高了成功识别特定情绪状态（如心境正常和抑郁）的准确性。特别是，使用即时线性特征集的特异性为74.44%，同时使用非线性特征集的特异性为99.56%。此外，与TAT诱发方案相比，IAPS情感诱发导致了更具区分性的程序。
尽管存在个体差异，但在抑郁和心境正常状态下发现了显著的即时心跳特征模式。所提出的基于点过程的心率变异性（HRV）非线性方法在双相情感障碍患者情绪评估领域提供了一个有前景的应用。
