Abstract
Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.
Highlights
Blood pressure (BP) is one of the vital signs that provide fundamental health information of the patient
BP varies between systolic BP (SBP), which is an increase in pressure caused by systolic contraction of the heart, and diastolic BP (DBP), which is the low pressure between SBPs
We developed an end-to-end deep learning model for BP estimation without feature extraction and used three physiological signals (ECG, PPG, and BCG) that were used in previous studies for estimating BP
Summary
Blood pressure (BP) is one of the vital signs that provide fundamental health information of the patient. The gold standard of BP monitoring is a sphygmomanometer, which is usually used in the physician’s office. BP measurement in medical circumstances may be incorrect because, in a clinical setting, some patients exhibit a higher BP than usual, which is known as the “white-coat effect.”. The arterial BP (ABP) is considered as a gold standard for continuous BP monitoring, which is performed in the intensive care unit. Measurement of ABP can cause side effects such as bleeding and infection because it is an invasive method that requires the insertion of an intravascular cannula needle. It is difficult to measure ABP in daily life because it requires a clinical setting
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