Bioengineering and Robotics Research Centre E. Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.
Department of Neurological, Biomedical and Movement Sciences, University of Verona, Verona, Italy.
Med Biol Eng Comput. 2019 Jan;57(1):123-134. doi: 10.1007/s11517-018-1869-1. Epub 2018 Jul 14.
Emphatic doctor-patient communication has been associated with improved psycho-physiological well-being involving cardiovascular and neuroendocrine responses. Nevertheless, a comprehensive assessment of heartbeat linear and nonlinear dynamics throughout the communication of a life-threatening disease has not been performed yet. To this extent, we studied linear heartbeat dynamics through the extraction of time-frequency domain measurements, as well as heartbeat nonlinear and complex dynamics through novel approaches to compute multi-scale and multi-lag series analyses: namely, the multi-scale distribution entropy and lagged Poincaré plot symbolic analysis. Heart rate variability series were recorded from 54 healthy female subjects who were blind to the aim of the experiment. Participants were randomly assigned into two groups: 27 subjects watched a video where an oncologist discloses the diagnosis of a cancer metastasis to a patient, whereas the remaining 27 watched the same video including four additional supportive comments by the clinician. Considering differences between the beginning and the end of each communication video, results from non-parametric Wilcoxon tests demonstrated that, at a group level, significant differences occurred in heartbeat linear and nonlinear dynamics, with lower complexity during nonsupportive communication. Furthermore, a support vector machine algorithm, validated using a leave-one-subject-out procedure, was able to discern the supportive experience at a single-subject level with an accuracy of 83.33% when nonlinear features were considered, dropping to 51.85% when using standard HRV features only. In conclusion, heartbeat nonlinear and complex dynamics can be a viable tool for the psycho-physiological evaluation of supportive doctor-patient communication. Graphical Abstract Scheme of the three main stages of the study: signal acquisition during doctor-patient communication, ECG signal processing and pattern recognition results.
强调医患沟通与改善涉及心血管和神经内分泌反应的心理生理健康有关。然而，迄今为止，尚未对危及生命的疾病交流过程中的心跳线性和非线性动力学进行全面评估。在这方面，我们通过提取时频域测量值来研究心跳线性动力学，以及通过计算多尺度和多滞后期序列分析的新方法来研究心跳非线性和复杂动力学：即多尺度分布熵和滞后庞加莱图符号分析。从 54 名健康女性参与者的心率变异性系列中记录数据，这些参与者对实验的目的一无所知。参与者被随机分配到两组：27 名参与者观看了一段视频，视频中一名肿瘤学家向患者透露了癌症转移的诊断，而其余 27 名参与者观看了相同的视频，其中包括临床医生的另外四条支持性评论。考虑到每个交流视频开始和结束之间的差异，非参数 Wilcoxon 检验的结果表明，在组水平上，心跳线性和非线性动力学存在显著差异，在非支持性交流期间复杂性降低。此外，使用留一受试者外验证程序验证的支持向量机算法能够以 83.33%的准确率在单个受试者水平上识别支持性体验，而仅使用标准 HRV 特征时准确率降至 51.85%。总之，心跳非线性和复杂动力学可以成为支持性医患沟通的心理生理评估的有效工具。
