van Es Valerie A A, de Lathauwer Ignace L J, Kemps Hareld M C, Handjaras Giacomo, Betta Monica
MoMiLab Research Unit, IMT School for Advanced Studies Lucca, 55100 Lucca, Italy.
Department of Cardiology, Máxima Medical Centre, 5504 DB Veldhoven, The Netherlands.
Bioengineering (Basel). 2024 Oct 19;11(10):1045. doi: 10.3390/bioengineering11101045.
Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.
夜间交感神经活动亢进是心血管疾病的早期指标，这凸显了对睡眠期间自主神经功能进行可靠的远程患者监测（RPM）的重要性。为了有效，RPM系统必须准确、非侵入性且具有成本效益。本综述评估了用于跟踪夜间自主神经系统（ANS）活动的非侵入性技术、指标和算法，评估了它们与心血管的相关性以及整合到RPM系统中的可行性。一项系统检索从最初的169篇出版物中确定了18项相关研究，并提取了有关研究设计、人群特征、技术类型和心血管影响的数据。所综述的方法包括电极（例如脑电图（EEG）、心电图（ECG）、多导睡眠图（PSG））、光学传感器（例如光电容积脉搏波描记法（PPG）、外周动脉张力（PAT））、心冲击图（BCG）、摄像头、雷达和加速度计。心率变异性（HRV）和血压（BP）成为RPM最有前景的指标，可全面了解睡眠期间的ANS功能和血管健康状况。虽然电极可提供精确的HRV数据，但它们仍然具有侵入性，而诸如PPG之类的光学传感器展示了多模态监测的潜力，包括HRV、SpO2以及动脉僵硬度和血压的估计。诸如BCG和摄像头之类的非侵入性方法在估计心率和呼吸率方面很有前景，但不太适合连续的HRV监测。总之，HRV和BP是RPM最可行的指标，基于PPG的系统在非侵入性、连续监测多种模态方面具有重大潜力。需要进一步研究以提高准确性、可行性，并对照自主神经功能的直接测量方法（如微神经ography）进行验证。
