Gyrosphygmogram-Based Blood Pressure Estimation: A Comparative Study of Pulse Transit Time and CNN–LSTM Methods

Goal: Cuffless blood pressure (BP) monitoring is essential for continuous cardiovascular health assessment. This study evaluates the potential of a novel wearable signal, gyrosphygmogram (GSG), by comparing conventional pulse transit time (PTT)-based methods with a deep learning framework. Methods: ECG, PPG, and wrist-based GSG signals were simultaneously acquired from 20 healthy adults using Shimmer sensors, with the Omron 10 Series sphygmomanometer as the reference. PTT was computed across multiple timing pairs (ECG–PPG, ECG–GSG, and GSG–PPG) and modeled using linear and nonlinear regression equations. A multimodal deep learning network was developed to fuse temporal and morphological features from the three signals. Model performance was assessed against three different validation standards. Results: The deep learning model outperformed conventional regression, with GSG-ECG fusion yielding Mean Absolute Errors (MAEs) of 2.89 mmHg (systolic) and 2.2 mmHg (diastolic). Conclusions: Incorporating GSG into multimodal deep learning substantially improves cuffless BP estimation compared with traditional PTT. These findings highlight GSG’s potential for enabling next-generation wearable devices with clinically validated accuracy.