Name
Title | ||
|---|---|---|
Beat-To-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21010096 | SENSORS |
Keywords | DocType | Volume |
cuffless blood pressure, ballistocardiogram, long short-term memory, general blood pressure estimation | Journal | 21 |
Issue | ISSN | Citations |
1 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongseok Lee | 1 | 2 | 1.74 |
| Hyunbin Kwon | 2 | 3 | 1.80 |
| Dongyeon Son | 3 | 0 | 0.68 |
| Heesang Eom | 4 | 4 | 1.46 |
| Cheolsoo Park | 5 | 6 | 3.25 |
| Yonggyu Lim | 6 | 2 | 0.73 |
| Chulhun Seo | 7 | 2 | 5.16 |
| Kwang Suk Park | 8 | 266 | 46.43 |