Principle Investigator: Dr. Md Shohidul ISLAM & Dr. Md Sadek ALI
Project Co-ordinator: Professor Derek HO
Data Coverage:
In this study, we will develop a novel, efficient, generalized, and feature engineering exempted data-driven DL approach for estimation of BP employing a single channel PPG signal recorded from diverse subjects with and without CVD complications. A modified long-term recurrent convolutional network (LRCN) based DL framework is proposed that combines the strengths of CNN and bidirectional LSTM (BiLSTM) to simultaneously infer systolic blood pressure (SBP), and diastolic blood pressure (DBP) by exploiting a single sensor unit data (e.g., PPG sensor data). The model benefits from data-driven feature extraction by leveraging the joint framework of CNN-BiLSTM, resulting in an efficient and precise solution for wearable healthcare IoT applications. The proposed LRCN framework achieves several goals and contributes to the advancement of sciences: 1) The proposed model predicts SBP and DBP simultaneously, offering a cost-effective solution with minimal sensor units. 2) The development of a sophisticated BP framework eradicates the requirement for individual model training. 4) The LRCN model with improved average MAE and SD for BP estimation on a larger population can be a tremendous achievement as compared with recently reported research. 4) Continuous monitoring of BP enhances the potential for initial detection of human health conditions and leads to improved outcomes.
In this study, we will develop a novel, efficient, generalized, and feature engineering exempted data-driven DL approach for estimation of BP employing a single channel PPG signal recorded from diverse subjects with and without CVD complications. A modified long-term recurrent convolutional network (LRCN) based DL framework is proposed that combines the strengths of CNN and bidirectional LSTM (BiLSTM) to simultaneously infer systolic blood pressure (SBP), and diastolic blood pressure (DBP) by exploiting a single sensor unit data (e.g., PPG sensor data). The model benefits from data-driven feature extraction by leveraging the joint framework of CNN-BiLSTM, resulting in an efficient and precise solution for wearable healthcare IoT applications. The proposed LRCN framework achieves several goals and contributes to the advancement of sciences: 1) The proposed model predicts SBP and DBP simultaneously, offering a cost-effective solution with minimal sensor units. 2) The development of a sophisticated BP framework eradicates the requirement for individual model training. 4) The LRCN model with improved average MAE and SD for BP estimation on a larger population can be a tremendous achievement as compared with recently reported research. 4) Continuous monitoring of BP enhances the potential for initial detection of human health conditions and leads to improved outcomes.