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在动脉血压和光电容积脉搏波波形中使用时间地标进行特征提取的工具

Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms.

作者信息

Pal Ravi, Rudas Akos, Williams Tiffany, Chiang Jeffrey N, Barney Anna, Cannesson Maxime

机构信息

Department of Anesthesiology & Perioperative Medicine, University of California, Los Angeles, CA, USA.

Department of Computational Medicine, University of California, Los Angeles, CA, USA.

出版信息

medRxiv. 2025 Mar 21:2025.03.20.25324325. doi: 10.1101/2025.03.20.25324325.

Abstract

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

摘要

动脉血压(ABP)和光电容积脉搏波描记法(PPG)波形都包含用于预防和治疗心血管疾病的重要生理信息。从这些波形中提取的特征具有多种临床应用,包括预测高血压和低血压、从ABP估计心输出量以及从PPG监测血压和痛觉。然而,缺乏标准化的特征提取工具限制了它们的探索和临床应用。在本研究中,我们提出了一种自动特征提取工具,该工具首先使用迭代包络均值法检测ABP和PPG波形每个心动周期内特征点的时间位置,包括收缩期起始点、收缩期峰值、重搏波切迹和舒张期峰值。然后,基于这些特征点,每个心动周期提取852个特征,涵盖时域、统计域和频域。使用来自包含17327名患者的大型围手术期数据集(MLORD数据集)的ABP和PPG波形评估该工具检测特征点的能力。我们分析了34267个ABP波形心动周期和33792个PPG波形心动周期。此外,为了评估该工具的实时特征点检测能力,我们回顾性分析了从飞利浦IntelliVue MX800患者监护仪收集的3000个ABP和PPG波形心动周期。根据经验丰富的研究人员的标记评估该工具的检测性能,ABP和PPG的平均F1分数和错误率如下:(1)在MLORD数据集上:收缩期起始点(99.77%,0.35%和99.52%,0.75%),收缩期峰值(99.80%,0.30%和99.56%,0.70%),重搏波切迹(98.24%,2.63%和98.72%,1.96%),以及舒张期峰值(98.59%,2.11%和98.88%,1.73%);(2)在实时数据上:收缩期起始点(98.18%,3.03%和97.94%,3.43%),收缩期峰值(98.22%,2.97%和97.74%,3.77%),重搏波切迹(97.72%,3.80%和98.16%,3.07%),以及舒张期峰值(98.04%,3.27%和98.08%,3.20%)。该工具在支持ABP和PPG波形特征的临床应用以及促进基于特征机器学习模型用于这些波形衍生特征起关键作用的各种临床应用方面具有巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c4e/11957180/ee554fb78a36/nihpp-2025.03.20.25324325v1-f0001.jpg

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