Haque Rafita, Wang Chunlei, Pala Nezih
Electrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USA.
Mechanical and Aerospace Engineering Department, University of Miami, Miami, FL 33136, USA.
Sensors (Basel). 2025 Jul 24;25(15):4574. doi: 10.3390/s25154574.
Continuous blood pressure (BP) monitoring provides valuable insight into the body's dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring.
连续血压监测能够深入了解身体在各种生理状态下（如体育活动、情绪压力、姿势变化和睡眠）的动态心血管调节情况。连续血压监测可捕捉收缩压和舒张压的不同变化，反映自主神经系统活动、血管顺应性和昼夜节律。这有助于早期识别异常血压趋势，并能及时进行诊断和干预，以降低心血管疾病（如高血压、中风、心力衰竭和慢性肾病）以及慢性应激或焦虑症的风险。为便于进行连续血压监测，我们提出了一个基于人工智能的估计框架。所提出的框架首先采用一种由专家驱动的特征工程方法，从基于光电容积脉搏波描记图（PPG）的动脉脉搏波形（APW）中系统地提取生理特征。提取的特征包括脉搏率、上升/下降时间、脉宽、斜率、强度变化和波形面积。这些特征与人口统计学数据（年龄、性别、身高、体重、BMI）融合，以增强模型在不同人群中的稳健性和准确性。该框架利用Tab-Transformer学习丰富的特征嵌入，然后通过由CatBoost、XGBoost和LightGBM组成的集成机器学习框架进行处理。在一个包含1000名受试者的数据集上进行评估时，该模型的收缩压平均绝对误差（MAE）为3.87 mmHg，舒张压平均绝对误差为2.50 mmHg，符合英国高血压学会（BHS）A级和美国医疗仪器促进协会（AAMI）标准。所提出的架构推动了用于动态心血管健康监测的非侵入性、基于人工智能的解决方案。
