Deep learning model with individualized fine-tuning for dynamic and beat-to-beat blood pressure estimation

Jingyuan Hong, Jiasheng Gao, Qing Liu, Yuanting Zhang, Yali Zheng*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)

Abstract

Deep learning (DL) models have demonstrated great potential in cuffless blood pressure (BP) estimation under static conditions, while the performance under dynamic conditions was still not fully validated. This study developed a DL model using population data for training and followed by individualized fine-tuning to directly learn features from multi-sensory signals including electrocardiogram (ECG), photoplethysmogram (PPG) and PPG derivatives for beat-to-beat BP estimation under water drinking. 25 healthy subjects were recruited, and the leave-one-subject-out approach was used to evaluate the model performance. The results showed that individualized fine-tuning using a small amount of individual baseline data did not change the tracking capability of the model, while can largely reduce the individual bias in dynamic BP estimation, with the mean absolute errors decreased from 13.43 to 9.49 mmHg and 8.48 to 5.54 mmHg for systolic BP and diastolic BP, respectively. It was also found that the model presented better results around the baseline BP levels than that at larger deviations from the baseline, indicating that future work should incorporate individual dynamic data in the fine-tuning to improve dynamic BP estimation further.

Original languageEnglish
Title of host publication2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665403627
DOIs
Publication statusPublished - 27 Jul 2021
Event17th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021 - Virtual, Online, Greece
Duration: 27 Jul 202130 Jul 2021

Publication series

Name2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021
Volume2021-January

Conference

Conference17th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021
Country/TerritoryGreece
CityVirtual, Online
Period27/07/2130/07/21

Keywords

  • Cuff-less and beat-to-beat blood pressure estimation
  • Deep learning
  • Dynamic conditions
  • Fine-tuning

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