IRIDIA, Université Libre de Bruxelles, Bruxelles, Belgium.
Department of Cardiology, UMONS (Université de Mons), Mons, Belgium.
Acta Cardiol. 2023 Aug;78(6):648-662. doi: 10.1080/00015385.2023.2177371. Epub 2023 Feb 21.
The role of the autonomic nervous system in the onset of supraventricular and ventricular arrhythmias is well established. It can be analysed by the spontaneous behaviour of the heart rate with ambulatory ECG recordings, through heart rate variability measurements. Input of heart rate variability parameters into artificial intelligence models to make predictions regarding the detection or forecast of rhythm disorders is becoming routine and neuromodulation techniques are now increasingly used for their treatment. All this warrants a reappraisal of the use of heart rate variability for autonomic nervous system assessment.Measurements performed over long periods such as 24H-variance, total power, deceleration capacity, and turbulence are suitable for estimating the individual basal autonomic status. Spectral measurements performed over short periods provide information on the dynamics of systems that disrupt this basal balance and may be part of the triggers of arrhythmias, as well as premature atrial or ventricular beats. All heart rate variability measurements essentially reflect the modulations of the parasympathetic nervous system which are superimposed on the impulses of the adrenergic system. Although heart rate variability parameters have been shown to be useful for risk stratification in patients with myocardial infarction and patients with heart failure, they are not part of the criteria for prophylactic implantation of an intracardiac defibrillator, because of their high variability and the improved treatment of myocardial infarction. Graphical methods such as Poincaré plots allow quick screening of atrial fibrillation and are set to play an important role in the e-cardiology networks. Although mathematical and computational techniques allow manipulation of the ECG signal to extract information and permit their use in predictive models for individual cardiac risk stratification, their explicability remains difficult and making inferences about the activity of the ANS from these models must remain cautious.
自主神经系统在室上性和室性心律失常的发生中的作用已得到充分证实。可以通过动态心电图记录的心率自发性来分析,通过心率变异性测量。将心率变异性参数输入人工智能模型以预测节律障碍的检测或预测正变得常规,并且神经调节技术现在越来越多地用于其治疗。所有这些都需要重新评估心率变异性在自主神经系统评估中的应用。在长时间段(如 24 小时方差、总功率、减速能力和湍流)进行的测量适用于估计个体基础自主状态。在短时间段内进行的频谱测量提供了有关破坏这种基础平衡的系统动力学的信息,并且可能是心律失常、房性或室性早搏的触发因素之一。所有心率变异性测量本质上都反映了迷走神经的调制,这种调制叠加在肾上腺素能系统的冲动上。尽管心率变异性参数已被证明对心肌梗塞患者和心力衰竭患者的风险分层有用,但由于其高变异性和心肌梗塞的治疗改善,它们不是预防性植入心内除颤器的标准的一部分。图形方法,如 Poincaré 图,允许快速筛选心房颤动,并将在电子心脏病学网络中发挥重要作用。尽管数学和计算技术允许对 ECG 信号进行操作以提取信息,并允许将其用于个体心脏风险分层的预测模型中,但它们的可解释性仍然很困难,并且必须谨慎地从这些模型中推断出 ANS 的活动。