dc.description.abstract | In modern society, cardiovascular disease is the leading cause of human deaths, and blood pressure monitoring is essential for prevention, diagnosis, assessment, and treatment. Traditional blood pressure measurement mainly relies on oscillometric method, which cannot achieve high-frequency blood pressure monitoring. Furthermore, these methods require the application of pressure on the artery using a cuff, which can cause discomfort to the user. In order to address these challenges and achieve unparalleled convenience, numerous studies have employed the use of photoplethysmography (PPG) and electrocardiogram (ECG) signals, which can be acquired from wearable devices, to estimate blood pressure. This advancement allows for cuffless blood pressure monitoring.
This study is based on the Transformer model, a deep learning method, to analyze signals from PPG and ECG for extracting blood pressure-related features. To accurately capture local semantic information in the signals, we partitioned the signals into partially overlapping patches, which were treated as individual tokens inputted into the model. Moreover, considering the variabilities observed among individuals in physiological signals, we incorporated a calibration procedure and additional information such as age, gender, and height as reference factors for the model. The effectiveness of the proposed algorithm was evaluated on VitalDB datasets, which consists of data from 1,437 patients. In the VitalDB test set, our algorithm achieved mean errors of -0.71 ± 6.91 mmHg for systolic blood pressure and -0.69 ± 4.22 mmHg for diastolic blood pressure, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI) for blood pressure measurement devices. These results further validate the efficacy of the algorithm proposed in this study. | en_US |