Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 1; p. 96
• Main Authors: Lee, Dongseok, Kwon, Hyunbin, Son, Dongyeon, Eom, Heesang, Park, Cheolsoo, Lim, Yonggyu, Seo, Chulhun, Park, Kwangsuk
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 25.12.2020; MDPI AG
• Subjects: ballistocardiogram; Blood Pressure; Blood Pressure Determination; cuffless blood pressure; general blood pressure estimation; Humans; Letter; long short-term memory; Neural Networks, Computer; Photoplethysmography; Pulse Wave Analysis; Reproducibility of Results; general blood pressure estimation; cuffless blood pressure; ballistocardiogram; long short-term memory
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21010096

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.