Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network
- Authors
- Lee, Dongseok; Kwon, Hyunbin; Son, Dongyeon; Eom, Heesang; Park, Cheolsoo; Lim, Yonggyu; Seo, Chulhun; Park, Kwangsuk
- Issue Date
- Jan-2021
- Publisher
- MDPI
- Keywords
- cuffless blood pressure; ballistocardiogram; long short-term memory; general blood pressure estimation
- Citation
- SENSORS, v.21, no.1, pp.1 - 15
- Journal Title
- SENSORS
- Volume
- 21
- Number
- 1
- Start Page
- 1
- End Page
- 15
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/40378
- DOI
- 10.3390/s21010096
- ISSN
- 1424-8220
- Abstract
- 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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