An efficient hybrid LSTM-ANN joint classification-regression model for PPG based blood pressure monitoring

This paper investigates the importance of classification in optimizing the estimation accuracy of blood pressure (BP) using photoplethysmography (PPG) signal features, with the aim of balancing efficiency and performance. Accordingly, the experimental design of this study was employed by constructing various artificial neural network (ANN) and long short-term memory (LSTM) topologies such as conventional single-stage models and classification-regression schemes including two-stage models, joint learning models, and the hybrid LSTM-ANN model. All models were tested on 40 subjects from the Physionet's MIMIC II database. With only 4 features, the proposed hybrid LSTM-ANN model achieved the best MAE ± SD of 3.39 ± 5.47 and 1.79 ± 3.72 mmHg for SBP and DBP, respectively. Furthermore, the single-stage and the two-stage ANN-based models were selected to be embedded in STM32 Microcontroller Unit (MCU) for real-time predictions of BP. The two-stage classification-regression ANN model showed superior performance and robustness in real-time testing on six subjects, achieved an MAE ± SD of 1.41 ± 1.29 mmHg, resulting in an 83.5% reduction of MAE compared with the traditional single-stage ANN model. Overall, the data presented in this work substantiate the significant role of classification in improving regression accuracy in both ANN and LSTM-based models. Compared to other state-of-the-art models, the hybrid LSTM-ANN with a joint classification-regression approach was found to be the optimal architecture to achieve the best performance and a trade-off between memory usage, complexity, and latency with acceptable BP prediction accuracy.