Accurate Blood Pressure Prediction using Korotkoff Sound and Cuff Pressure: A Pilot Study

Blood pressure (BP) serves as a critical indicator of cardiovascular health. Hypertension, a grave medical condition, manifests when the body’s BP surpasses the threshold of 140/90 mmHg, leading to life-threatening risks such as heart attacks and strokes. Many commercial digital BP devices based on Oscillometric method are often less accurate which may lead to inaccurate diagnosis, especially in home settings.This research attempts to develop an accurate BP prediction method using deep learning model over korotkoff sound and cuff pressure. This is a pilot study to validate the method of BP prediction and to show proof of concept. A digital BP measurement prototype device was developed on the principle of auscultation, renowned as the ’gold standard’ in manual BP measurement, and it is used for the sole purpose of capturing excellent quality korotkoff sound and cuff pressure. Using this prototype device, data was collected from a cohort of over 115 subjects, with measurements conducted under the supervision of medical professionals.A Convolutional Neural Network (CNN) based model was trained on this captured data to discern Korotkoff peaks, enabling an exact prediction of corresponding systolic and diastolic BP values. After comparing the predictions with ground truth, the validation results show mean absolute error and standard deviation error for systolic and diastolic errors as 4.06 ± 5.35 mmHg and 3.89 ± 4.51 mmHg, respectively. This promising outcome underscores the potential of accurate BP prediction using auscultatory method and deep learning as a valuable tool.