Blood pressure estimation using a hybrid deep learning model

Anumaan Jain

Abstract


Traditional blood pressure (BP) measuring systems have a number of disadvantages, such being invasive, cuff-based, or needing manual procedures. There is a lot of interest in developing non-invasive, cuff-less, and continuous BP measuring methods based on physiological measurements. However, in the presence of noise or signal distortion, extracting characteristics from signals is difficult using these approaches. Inaccuracies in feature extraction lead to errors in BP estimate when employing machine learning; hence, this work investigates the use of raw signals as a direct input to a deep learning model.To compare with typical machine learning models that employ photoplethysmogram and ECG data, a hybrid deep learning model is constructed that uses both raw signals and physical variables (age, height, weight, and gender). This hybrid model performs best in terms of diastolic blood pressure (DBP) and systolic blood pressure (SBP), with mean absolute errors of 3.23 4.75 mmHg and 4.43 6.09 mmHg, respectively. DBP and SBP fulfil the British Hypertension Society's Grade A and Grade B performance levels, respectively.

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