One-Month Evaluation of Blood Pressure Estimation Using a Fine-Tuned Model With Wristband-Type Photoplethysmograms

In the quest for a blood pressure (BP) measurement method that is applicable in daily life, deep learning-based BP estimation models utilizing photoplethysmograms (PPGs) have recently been introduced. To allow the routine deployment of BP estimation models that rely on wearable sensing PPGs, they should be able to sustain performance over an extended period. However, achieving stability is difficult due to variations in the distribution of PPG data caused by shifts in sensor positions and human conditions. This study assessed the performance of deep learning-based BP estimation models that were pretrained on a large-scale dataset and explored their adaptability and adjustment frequency during fine-tuning with wearable sensed small-scale data over a one-month period. Two distinct datasets were utilized: the publicly available Multiparameter Intelligent Monitoring in Intensive Care III Waveform Database Matched Subset version 1.0 and a private dataset recorded from 11 participants over four days (one day per week) using a customized wearable wristband-type PPG device. The results indicated the superior performance of the fine-tuned models compared with the non-fine-tuned models. Tuning the models every week resulted in greater performance than tuning only once in the first week. Balancing convenience and performance continues to be a practical challenge in this research field. Moreover, even the most accurate fine-tuned model does not consistently satisfy medical standards, presenting a hurdle for medical applications. This study revealed the effectiveness and adaptability of pretrained fine-tuned BP estimation models for long-term wearable sensing data; however, enhancing the estimation performance remains a challenge.