| dc.description.abstract | The wide availability of public biomedical data, progressive wearable sensors, electronic health records, the advancement of machine learning techniques, and powerful computer resources contribute to the significant interest rise in artificial intelligence-based healthcare development. In this dissertation, we focus on blood pressure (BP) estimation using various deep learning techniques, including a Fully Connected Neural Network (FCNN), a Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN). This dissertation explores the efficacy of personalized deep learning-based BP estimation systems in several settings. First, we develop an LSTM model for estimating systolic BP (SBP) and diastolic BP (DBP) values from a feature set extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Although this model could outperform the traditional machine learning models, we came up with a simpler architecture, an FCNN model, to estimate BP from a feature set extracted from PPG signal only, which not only performs better but also justify the practicality of an ECG-free system. Second, we introduce a feature engineering-free BP estimation system by utilizing CNN as the automatic feature extraction in our model. This approach is taken to tackle the problem of feature extraction failure due to heavy noises on the signal. Furthermore, we train a self-supervised LSTM autoencoder model to not only predict the discrete value of the SBP and DBP but the whole arterial BP (ABP) waveform. This dissertation demonstrates how using deep learning-based models for featureless BP estimation not only avoids the expensive feature extraction and selection procedure but also reveals unexpected and intuitive patterns in the input data. Lastly, a real-time BP estimation mobile and watch application is presented in this dissertation, providing feasibility on its development. Overall, this dissertation′s findings supported the notion that deep learning techniques used for cuffless BP estimation could enhance and expand existing regression models, open up new research opportunities, and contribute in the building of real-time health monitoring applications. | en_US |