Pulse wave analysis of carotid artery diameter waveforms estimated blood pressure with MAE of 3.3±4.1 mmHg during fluctuations >25 mmHg and 4.2±5.3 mmHg after 2+ days.
Does a machine learning model using features from carotid artery diameter waveforms accurately estimate blood pressure compared to a reference device?
Machine learning models utilizing features from ultrasound-derived carotid artery diameter waveforms can estimate blood pressure with a low mean absolute error, maintaining effectiveness over multiple days.
Absolute Event Rate: 0% vs 0%
Blood pressure estimation through pulse wave analysis (PWA) aims to establish the relationship between features of pulse waveforms and blood pressure. This study is the first to investigate the connection between features of carotid artery diameter waveforms and variations in blood pressure, as well as to develop a blood pressure estimation model based on these features. A dataset was constructed from 14 subjects, with data collected across various physiological states and time points. For each subject, carotid artery diameter waveforms were measured using ultrasound, while synchronous blood pressure data were recorded with a reference device. A total of 52 morphological features were extracted from the diameter waveforms and their first and second derivatives. The influence of different models and feature combinations on blood pressure estimation was analyzed using various machine learning approaches. Ultimately, optimal models were developed for each subject to dynamic blood pressure fluctuations. On independent test data where blood pressure fluctuations exceeded 25 mmHg, the mean absolute error (MAE) of the estimates was 3.3 ± 4.1 mmHg. Even after a period of two days or more, the models remained effective, yielding a MAE of 4.2 ± 5.3 mmHg.
Xu et al. (Sun,) reported a other. Pulse wave analysis of carotid artery diameter waveforms estimated blood pressure with MAE of 3.3±4.1 mmHg during fluctuations >25 mmHg and 4.2±5.3 mmHg after 2+ days.