Machine Learning of Ultrasound Data: Cardiovascular Parameters Detection Using Carotid Artery Measurements

Abstract

Background & Objective
Cardiovascular diseases (CVDs) are the leading cause for death globally nowadays. Pulse wave velocity (PWV), a marker of arterial stiffness, is an important predictor of CVD risk. In precedent work, carotid artery data was collected with ultrasound to estimate the PWV with a digital signal processing (DSP) pipeline. As a potential alternative to the DSP-based approach, this thesis studies the applicability of machine learning(ML) for the estimation of carotid artery motion (diameter, distension, etc.) and explores to what extent neural networks can exploit the ultrasound data to extract relevant biomarker information.
Methods:
This thesis proposes a ML pipeline that processes the ultrasound data in a different perspective than the DSP approach. The ML pipeline consists of four modules (neural networks & post-processing) to: 1) segmentation based on cardiac cycle (CC), 2) detect the region of interest (ROI) of artery in the ultrasound data, 3) tracking the artery diameter and 4) post processing to estimate cardiac parameters e.g. pulse arrival time (PAT), an essential part of PWV estimation. Exploiting the features of the artery-lumen structure and time-evolving characteristics of collected ultrasound signal, the designed ML pipeline can acquire cardiac markers spatially and temporally with irregular kernels and sliding mechanism, decompose the complicated estimation into compact sub-modules.
Results:
The results show that the ML approach can successfully estimate the artery diameters and reserve important waveform features (max-slope moment) of the artery diameter, and can infer the CC markers without ECG data as a segmenting event for heart cycles. Thus, the PAT can be computed as the time difference of the max-slope moments of inferred artery diameter and detected CC markers. According to the numerical results, the PAT can be estimated on the average for an ultrasound data recording (120s), and the correlation coefficient of label PAT (computed from estimated parameters of DSP pipeline and ECG data) and estimated PAT (ML pipeline) is 0.8250. This indicates a good correlation and hence proves the effectiveness of the mean PAT estimation.
Conclusion:
In conclusion, the proposed ML pipeline can effectively estimate mean PAT, and demonstrate the feasibility to estimate PWV as a relevant cardiovascular marker using only ultrasound data of carotid arteries. Apart from the PAT, the heart rate can also be possibly tracked via intermediate results of the ML pipeline (CC markers). From the future perspective, the potential of phase information in the raw ultrasound data and further optimization are worth exploring, and the extension to hardware (e.g. chips, embedded system) can be implemented as a practical application.