SO-MoE: A Novel Self-Organizing Mixture of Experts Framework for Cuffless Blood Pressure Measurement

Cuffless blood pressure (BP) monitoring is a transformative technology that enables continuous noninvasive measurement of BP, which is critical for early diagnosis and prevention of cardiovascular diseases (CVD). However, current predictive models exhibit performance limitations across underrepresented demographics. This limitation is particularly pronounced in hypertensive populations, where the large BP volatility challenges accuracy of the model for BP estimation. Addressing these demographic disparities is crucial to ensure the objectivity and fairness of model for cuffless BP estimation. This paper proposes a self-organizing mixture of experts (SO-MoE) framework for BP estimation, which implements automated expert allocation mechanisms based on patient-specific characteristics. This specialized design enables precise expert selection for individualized BP estimation. We evaluated this system on the CAS-BP dataset of over seven hundred subjects and compared it with several deep learning models. The proposed system demonstrated promising performance for BP estimation with mean absolute errors (MAE) of 5.59 mmHg and 4.07 mmHg for systolic BP (SBP) and diastolic BP (DBP) respectively. Besides, the SO-MoE yielded estimation errors of -0.24 ± 7.85 mmHg for SBP and 0.33 ± 5.49 mmHg for DBP in the overall population. Key innovations of the proposed framework include reduced computational overhead, sustained temporal stability, and consistent performance across diverse demographic groups. These results establish a foundation for equitable and clinically accurate BP estimation, addressing the representation challenges inherent in cuffless BP monitoring.