# Blood Pressure Estimation Through Pulse Wave Analysis Using Features Extracted from Carotid Diameter Distension Waveforms

## TL;DR

This study explores how carotid artery waveform features can be used to estimate blood pressure with machine learning models.

## Contribution

The study is the first to investigate carotid diameter waveform features for blood pressure estimation and develop personalized models.

## Key findings

- A model using carotid artery waveform features achieved a mean absolute error of 3.3 ± 4.1 mmHg on test data with large blood pressure fluctuations.
- The models remained effective even after two or more days, with a mean absolute error of 4.2 ± 5.3 mmHg.

## Abstract

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.