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• W3117498368 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." @default.
• W3117498368 created "2021-01-05" @default.
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• W3117498368 date "2020-12-25" @default.
• W3117498368 modified "2023-10-14" @default.
• W3117498368 title "Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network" @default.
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• W3117498368 doi "https://doi.org/10.3390/s21010096" @default.
• W3117498368 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7795062" @default.
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• W3117498368 hasPublicationYear "2020" @default.
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