期刊
SENSORS
卷 21, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/s21010096
关键词
cuffless blood pressure; ballistocardiogram; long short-term memory; general blood pressure estimation
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2017R1A5A1015596]
- National Research Foundation of Korea [5199990614280] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
A cuffless blood pressure estimation model using deep learning algorithms was proposed, with features extracted from electrocardiogram, photoplethysmogram, and ballistocardiogram. The model achieved high accuracy in both one-day and multi-day tests, showing its potential for continuous blood pressure monitoring for patients with hypertension.
Continuous blood pressure (BP) monitoring is important for patients with hypertension. However, BP measurement with a cuff may be cumbersome for the patient. To overcome this limitation, various studies have suggested cuffless BP estimation models using deep learning algorithms. A generalized model should be considered to decrease the training time, and the model reproducibility should be taken into account in multi-day scenarios. In this study, a BP estimation model with a bidirectional long short-term memory network is proposed. The features are extracted from the electrocardiogram, photoplethysmogram, and ballistocardiogram. The leave-one-subject-out (LOSO) method is incorporated to generalize the model and fine-tuning is applied. The model was evaluated using one-day and multi-day tests. The proposed model achieved a mean absolute error (MAE) of 2.56 and 2.05 mmHg for the systolic and diastolic BP (SBP and DBP), respectively, in the one-day test. Moreover, the results demonstrated that the LOSO method with fine-tuning was more compatible in the multi-day test. The MAE values of the model were 5.82 and 5.24 mmHg for the SBP and DBP, respectively.
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