Kumar Saurav, Tayade Apakrita, Shrivastava Amber, Bhallamudi Ravi
Mechanical Engineering Department, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India.
Mechanical Engineering Department, Biomedical Engineering and Technology Innovation Centre (BETIC), Indian Institute of Technology Bombay, Mumbai, Maharashtra, India.
Sci Rep. 2025 Apr 23;15(1):14006. doi: 10.1038/s41598-025-98488-w.
Arterial pulse wave measurement is beneficial in clinical health assessment and is important for effectively diagnosing different types of cardiovascular disease. Computational pulse signal analysis utilizes sensors and signal processing techniques to understand, classify, and predict disease pulse patterns. However, the choice of sensor types impacts the measurement results. This study presents the first comprehensive quantitative comparison of three sensor modalities (acoustic, optical, and pressure) for radial pulse measurement, employing a novel multi-parameter analysis framework that combines time-domain, frequency-domain, and PRV measures. Among various available types, three types of sensors are compared: an acoustic sensor, an optical sensor, and a pressure sensor. Pulse wave signals were recorded from the radial artery of 30 participants using these three sensors, and the performance was evaluated using various feature extraction methods like time domain, frequency domain and pulse rate variability (PRV) measures. Further, statistical analysis (ANOVA) of the PRV measures was carried out to compare the differences in the means of the various PRV measures. Time and frequency domain features varied across sensor types, but no statistical differences were found in PRV measures across sensors. Based on the experimental results, the pressure sensor was found to perform better in capturing comprehensive wrist pulse information. The research provides evidence-based guidelines for sensor selection in pulse wave analysis applications. The findings have direct applications in developing wearable cardiovascular monitoring devices, where sensor choice critically impacts device accuracy and reliability. and clinical settings requiring pulse wave analysis for cardiovascular disease diagnosis.
动脉脉搏波测量对临床健康评估有益，对有效诊断不同类型的心血管疾病很重要。计算脉搏信号分析利用传感器和信号处理技术来理解、分类和预测疾病脉搏模式。然而，传感器类型的选择会影响测量结果。本研究首次对用于桡动脉脉搏测量的三种传感器模式（声学、光学和压力）进行了全面的定量比较，采用了一种结合时域、频域和脉搏率变异性（PRV）测量的新型多参数分析框架。在各种可用类型中，比较了三种类型的传感器：声学传感器、光学传感器和压力传感器。使用这三种传感器从30名参与者的桡动脉记录脉搏波信号，并使用时域、频域和脉搏率变异性（PRV）测量等各种特征提取方法评估性能。此外，对PRV测量进行了统计分析（方差分析），以比较各种PRV测量均值的差异。时域和频域特征因传感器类型而异，但在不同传感器的PRV测量中未发现统计学差异。基于实验结果，发现压力传感器在捕获全面的腕部脉搏信息方面表现更好。该研究为脉搏波分析应用中的传感器选择提供了循证指南。这些发现直接应用于开发可穿戴心血管监测设备，其中传感器的选择对设备的准确性和可靠性至关重要，也应用于需要进行脉搏波分析以诊断心血管疾病的临床环境。
