Campos Luciana A, Pereira Valter L, Muralikrishna Amita, Albarwani Sulayma, Brás Susana, Gouveia Sónia
Center of Innovation, Technology and Education-(CITE), Camilo Castelo Branco University (UNICASTELO) Sao Jose dos Campos, Brazil.
Front Physiol. 2013 Oct 7;4:279. doi: 10.3389/fphys.2013.00279.
Heart rate and blood pressure are the most important vital signs in diagnosing disease. Both heart rate and blood pressure are characterized by a high degree of short term variability from moment to moment, medium term over the normal day and night as well as in the very long term over months to years. The study of new mathematical algorithms to evaluate the variability of these cardiovascular parameters has a high potential in the development of new methods for early detection of cardiovascular disease, to establish differential diagnosis with possible therapeutic consequences. The autonomic nervous system is a major player in the general adaptive reaction to stress and disease. The quantitative prediction of the autonomic interactions in multiple control loops pathways of cardiovascular system is directly applicable to clinical situations. Exploration of new multimodal analytical techniques for the variability of cardiovascular system may detect new approaches for deterministic parameter identification. A multimodal analysis of cardiovascular signals can be studied by evaluating their amplitudes, phases, time domain patterns, and sensitivity to imposed stimuli, i.e., drugs blocking the autonomic system. The causal effects, gains, and dynamic relationships may be studied through dynamical fuzzy logic models, such as the discrete-time model and discrete-event model. We expect an increase in accuracy of modeling and a better estimation of the heart rate and blood pressure time series, which could be of benefit for intelligent patient monitoring. We foresee that identifying quantitative mathematical biomarkers for autonomic nervous system will allow individual therapy adjustments to aim at the most favorable sympathetic-parasympathetic balance.
心率和血压是疾病诊断中最重要的生命体征。心率和血压的特点是在瞬间、正常的昼夜以及数月至数年的长期内都具有高度的短期变异性。研究评估这些心血管参数变异性的新数学算法，在开发早期检测心血管疾病的新方法、建立具有可能治疗后果的鉴别诊断方面具有很大潜力。自主神经系统在对压力和疾病的一般适应性反应中起着主要作用。心血管系统多个控制回路途径中自主相互作用的定量预测可直接应用于临床情况。探索心血管系统变异性的新多模态分析技术可能会发现确定性参数识别的新方法。心血管信号的多模态分析可以通过评估其幅度、相位、时域模式以及对施加刺激（即阻断自主系统的药物）的敏感性来进行研究。因果效应、增益和动态关系可以通过动态模糊逻辑模型来研究，如离散时间模型和离散事件模型。我们期望提高建模的准确性，并更好地估计心率和血压时间序列，这可能有利于智能患者监测。我们预见，识别自主神经系统的定量数学生物标志物将允许进行个体治疗调整，以实现最有利的交感 - 副交感平衡。
