AI 中文总结
研究提出通用框架CORAL，通过相关形分析实现跨模态、抗噪声的生命体征率估计，经多数据库验证，其心率、呼吸率估计及心率变异性相关指标均达到较高精度。
AI 中文摘要
心率和呼吸率是关键的生命体征。我们提出CORAL以解决自动化连续生命体征监测面临的挑战，这些挑战包括：1）医院内针对更广泛人群（不同年龄、疾病状态）的监测；2）远程医疗和健康应用中的家庭监测（噪声、传感器位置问题）；3）跨数据模态（心电ECG、光电容积描记PPG、心音图SCG、生物阻抗BioZ等）的监测。我们重新引入短时自相关函数（STACF）以生成相关形（correloform）——一种用于追踪随时间变化的周期性、高可解释性的二维信号变换，并将CORAL定义为用于准周期信号鲁棒率估计的通用分析框架。我们对CORAL进行了严格的基准测试，以证明其在生物信号中的广泛适用性，这些适用性源于数学构造而非特定领域的工程设计，包括率估计、抗噪声能力、自动通道选择和信号质量指示。CORAL在FECGSYNDB数据库中实现了出色的瞬时无创胎儿心率一致性（F1=0.999），在ADFECGDB数据库中F1=1.000；针对重症监护病房（NICU）的困难新生儿，CORAL从可穿戴BioZ估计的呼吸率（RR）与同步有线Philips阻抗测量的呼吸间隔RR的相关系数r=0.858；在CEBS数据库中，CORAL的SCG心率与商用ECG相比r=0.990，在自由活动场景中r=0.970；无需QRS检测，CORAL的瞬时心率与Pan-Tompkins和NeuroKit2检测器的一致性，就像这两个检测器之间的一致性一样紧密（MIMIC-IV ICU ECG中，CORAL与两者的r均为0.87，而两者之间的r为0.88）；CORAL的心率标准差与基于间隔的心率变异性SDNN高度相关，即使来自机械SCG（CEBS中r=0.733）和ICU ECG队列（r=0.768）也是如此。
英文摘要
Heart rate and respiration rate are crucial vital signs. We present CORAL to address challenges in automating continuous vital sign monitoring 1) in-hospital across broader populations (age, disease states), 2) at-home in telehealth and wellbeing applications (noise, placement), and 3) across data modalities (ECG, PPG, SCG, BioZ, etc.). We re-introduce Short-Time Autocorrelation Functions (STACFs) to introduce the correloform - a highly interpretable 2D signal transformation for tracking periodicity over time - and define CORAL as a generic analytical framework for robust rate estimation of quasi-periodic signals. We rigorously benchmark CORAL to show ubiquitous application in biosignals via capabilities arising by mathematical construction rather than domain-specific engineering, including rate estimation, resilient noise handling, automatic channel selection, and signal quality indication. CORAL achieves excellent instantaneous noninvasive fetal HR agreement in FECGSYNDB (F1 = 0.999) and ADFECGDB (F1 = 1.000). With difficult NICU neonates, CORAL estimates RR from wearable BioZ at r = 0.858 against breath-interval RR from simultaneous wired Philips impedance. CORAL SCG HR on CEBS achieves r = 0.990 and in free-living activity r = 0.970 when compared against commercial ECG. Without QRS detection, CORAL's instantaneous HR agrees with Pan-Tompkins and NeuroKit2 detectors as closely as they agree with each other (MIMIC-IV ICU ECG r = 0.87 to each vs. 0.88 between them). CORAL's HR standard deviation correlates strongly with interval-based HRV SDNN, even from mechanical SCG (CEBS r = 0.733) and across an ICU ECG cohort (r = 0.768).
Commentsmain manuscript: 11 pages, 2 tables, 4 figures supplemental materials: 25 pages, 4 tabes, 31 figures
