This paper presents the Cardiorespiratory System Identification Lab (CRSIDLab), a MATLAB-based software tool for multivariate autonomic nervous system (ANS) evaluation through heart rate variability (HRV) analysis and cardiorespiratory system identification. Based on a graphical user interface, CRSIDLab provides a complete set of tools including pre-processing cardiorespiratory data (electrocardiogram, continuous blood pressure, airflow, and instantaneous lung volume), power spectral density estimation, and multivariable cardiorespiratory system model identification. Parametrized multivariate models can assess both HRV and baroreflex sensitivity (BRS) by considering the causal relationship from respiration to heart rate (or its reciprocal, R-to-R interval – RRI) and from systolic blood pressure to RRI, for instance. The impulse response, estimated from the model, is used as a mathematical tool to effectively open the inherently closed-loop nature of the cardiorespiratory system, allowing the investigation of the dynamic response between pairs of cardiorespiratory variables. This system modeling approach provides information on gain and temporal behavior regarding dynamics, such as the baroreflex, complementing traditional HRV, and BRS indices. The toolbox is presented and used to investigate autonomic function in sleep apnea. The results show that, while traditional HRV indices were unable to differentiate between apneic and non-apneic subjects, the autonomic descriptors obtained from the multivariate system identification techniques were able to show vagal impairment in apneic compared to non-apneic subjects. Thus, CRSIDLab can help promote the use of cardiorespiratory system identification as a potentially more sensitive measure of ANS activity than classical HRV analysis.

中文翻译：

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CRSIDLab：使用心肺识别进行多元自主神经系统分析的工具箱

本文介绍了心肺系统识别实验室（CRSIDLab），这是一种基于MATLAB的软件工具，可通过心率变异性（HRV）分析和心肺系统识别来评估多元自主神经系统（ANS）。基于图形用户界面，CRSIDLab提供了一套完整的工具，包括预处理心肺数据（心电图，连续血压，气流和瞬时肺体积），功率谱密度估计和多变量心肺系统模型识别。参数化多变量模型可以通过考虑从呼吸到心率（或其倒数，R到R间隔– RRI）和从收缩压到RRI的因果关系来评估HRV和压力反射敏感性（BRS）。冲动反应 从模型中估算出的值用作数学工具，可以有效地打开心肺系统的固有闭环特性，从而可以研究成对的心肺变量之间的动态响应。这种系统建模方法可提供有关增益的动态行为和时间行为的信息，例如压力反射，传统HRV和BRS指数的补充。介绍了该工具箱，并将其用于研究睡眠呼吸暂停中的自主神经功能。结果表明，尽管传统的HRV指数无法区分呼吸暂停和非呼吸暂停的受试者，但与非呼吸暂停的受试者相比，从多元系统识别技术获得的自主描述符能够显示出呼吸暂停的迷走神经损伤。因此，