Featureless Blood Pressure Estimation Using CNN and BiLSTM for IoT Devices

Madhu Kumar

Abstract


Continuous blood pressure (BP) measurement is essential for individual health monitoring. Photoplethysmography (PPG) is one of the most common noninvasive blood pressure measurement methods in the previous decade. To use characteristics retrieved from PPG, several techniques have been taken in diverse ways. We developed a continuous systolic and diastolic blood pressure (SBP and DBP) estimate method in this work without the need of feature engineering. The raw PPG signal was merely preprocessed before being input into our model, which is composed mostly of a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. The resultant SBP and DBP values are evaluated using the root-mean-squared error (RMSE) and mean absolute error (MAE) (MAE).

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