Neural Network-based Carotid-to-Femoral Pulse Wave Velocity Estimation Using PPG Signal
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The number of annual mortalities have been significantly increasing, with cardiovascular diseases (CVDs) accounting for third of them globally and becoming the leading cause of this phenomenon. Early Diagnosis of CVDs offers early intervention and consequently death prevention. Therefore, developing fast, accurate, and easily accessible diagnostic techniques is essential. For determining CVDs onset and development, several cardiovascular clinical features are utilized. The increment in arterial stiffness is a key clinical feature and a primary factor in predicting CVDs . A gold standard measurement for this clinical feature is evaluating the carotid-to-femoral pulse wave velocity (cf-PWV); however, its clinical assessment is very intrusive and complicated. One method of non-invasively, economical, and low-power-consuming monitoring the arterial stiffness is a straightforward optical technique called photoplethysmogram (PPG). In this project, PPG was incorporated with an artificial intelligence-based tool to extract central arterial stiffness by estimating cf-PWV to predict CVDs. Starting with a collected single PPG waveform at three different measurement sites: radial, digital, and brachial arteries. In addition to selected features, that were calculated using fiducial points from the PPG signal along with its first, second, and third derivatives, after feature selection and identification methods were applied. The prediction’s performance is assessed by the R2 (correlation coefficient) and MAPE (mean absolute percentage error) values, reflecting good results when it is close to 1 and 0, respectively. The results, using supervised deep learning model, and numerous iterations of the database’s training set, demonstrate good estimation performances utilizing the extracted features from PPG signal at the level of radial, digital, and brachial arteries with an R2 around 0.98, 0.97 and 0.95, and MAPE less than 1.71%, 1.88% and 2.22%, for each distal level respectively. Thereby, an innovative machine learning tool was incorporated to a non-invasive, accessible, and economical diagnostic technique making it user-friendly to predict CVDs.
Khouzama is a gifted senior student studying at The 30th Secondary School in Dammam. She has participated in various STEM-related Enrichment Programs. Also, she was part of one of the top research programs for high school students in Saudi Arabia, SRSI. Khouzama wants to pursue her career majoring in Computer Engineering in college. She aspires to be a distinguished person among a scientific community impacting people around her.