التفاصيل البيبلوغرافية
| العنوان: |
Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning. |
| المؤلفون: |
Nguyen DH, Chao PC, Yan HF, Tu TY, Cheng CH, Phan TP |
| المصدر: |
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2024 Apr 10; Vol. PP. Date of Electronic Publication: 2024 Apr 10. |
| Publication Model: |
Ahead of Print |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101604520 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2168-2208 (Electronic) Linking ISSN: 21682194 NLM ISO Abbreviation: IEEE J Biomed Health Inform Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013- |
| مستخلص: |
Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide effective pre-warning of possible preeclampsia of pregnant women. Towards frequent BP measurement, a PPG sensor device is utilized in this study as a solution to offer continuous, cuffless blood pressure monitoring frequently for pregnant women. PPG data were collected using a flexible sensor patch from the wrist arteries of 194 subjects, which included 154 normal individuals and 40 pregnant women. Deep-learning models in 3 stages were built and trained to predict BP. The first stage involves developing a baseline deep-learning BP model using a dataset from common subjects. In the 2 nd stage, this model was fine-tuned with data from pregnant women, using a 1-Dimensional Convolutional Neural Network (1D-CNN) with Convolutional Block Attention Module (CBAMs), followed by bi-directional Gated Recurrent Units (GRUs) layers and attention layers. The fine-tuned model results in a mean error (ME) of -1.40 ± 7.15 (standard deviation, SD) for systolic blood pressure (SBP) and -0.44 (ME) ± 5.06 (SD) for diastolic blood pressure (DBP). At the final stage is the personalization for individual pregnant women using transfer learning again, enhancing further the model accuracy to -0.17 (ME) ± 1.45 (SD) for SBP and 0.27 (ME) ± 0.64 (SD) for DBP showing a promising solution for continuous, non-invasive BP monitoring in precision by the proposed 3-stage of modeling, fine-tuning and personalization. |
| تواريخ الأحداث: |
Date Created: 20240410 Latest Revision: 20240411 |
| رمز التحديث: |
20240411 |
| DOI: |
10.1109/JBHI.2024.3386707 |
| PMID: |
38598377 |
| قاعدة البيانات: |
MEDLINE |
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