Exploring the feasibility of estimating the carotid-to-femoral pulse wave velocity using machine learning algorithms

Bahloul, Mohamed A., Vargas, Juan M., Belkhatir, Zehor and Laleg-Kirati, Taous-Meriem (2024) Exploring the feasibility of estimating the carotid-to-femoral pulse wave velocity using machine learning algorithms. Al-Jumaily, Adel, Crippa, Paolo, Mansour, Ali and Turchetti, Claudio (eds.) In Non-Invasive Health Systems based on Advanced Biomedical Signal and Image Processing. CRC Press. 36 pp . (doi:10.1201/9781003346678).

Abstract

This chapter discusses the potential use of machine learning-assisted assessment of arterial stiffness (AS), particularly carotid-to-femoral pulse wave velocity (cf-PWV), which is considered the gold-standard measurement of AS. The current method of measuring cf-PWV is considered challenging for clinicians and patients due to its operator dependency and potential inaccuracies. To overcome these limitations, different machine-learning pipelines were trained and tested using features extracted from peripheral pulse waveforms. Three modalities were investigated, including time domain-based features, frequency domain-based features, and semi-classical signal analysis-based features. Results show that these proposed features and algorithms have the potential to estimate cf-PWV and assess AS non-invasively, indicating the feasibility of using machine learning approaches as smart surrogate measures of vascular indicators and potential predictors for cardiovascular diseases.

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