End-To-End Personalized Cuff-Less Blood Pressure Monitoring Using ECG and PPG Signals

作者：Suhas BN, Rakshith Sharma Srinivasa, Yashas Malur Saidutta, Jaejin Cho, Ching-Hua Lee, Chouchang Yang, Yilin Shen, Hongxia Jin · 年份：2024 · DOI：10.1109/icassp48485.2024.10445970 · 被引用次数：12 · 研究领域：Non-Invasive Vital Sign Monitoring、ECG Monitoring and Analysis、Heart Rate Variability and Autonomic Control

Cuffless blood pressure (BP) monitoring offers the potential for continuous, non-invasive healthcare but has been limited in adoption by existing models relying on handcrafted features from ECG and PPG signals. To overcome this, researchers have looked to deep learning. Along these lines, in this paper, we introduce a novel end-to-end model based on transformers. Further, we also introduce a novel contrastive loss-based loss function for robust training. To study the limits of performance for our proposed ideas, we first study personalized models trained on large subject-specific datasets, and achieve an average mean absolute error of 1.08/0.68 mmHg for systolic (SBP) and diastolic BP (DBP) across all subjects while achieving a best case of 0.29/0.19 mmHg. Further, in the case where subject-specific data is scarce, we leverage transfer learning using multi-subject data, and show that our model outperforms State-of-the-Art (SOTA) methods across varying amounts of subject-specific data.