Remote health monitoring has attracted a lot of attention over the past decades to provide the opportunity for early detection of pathological health conditions. This early detection improves the quality of life for the patients and significantly reduces the burden on their family members. Moreover, this improvement reduces the socioeconomic consequences that are caused due to the disability of patients to work despite their health conditions. Wearable technologies offer a promising solution in pervasive health monitoring by relaxing the constraints concerning time and location. There are two major challenges in developing these technologies. The first challenge is real-time monitoring of the patients to fulfill early detection of life-threatening conditions using advanced machine learning techniques. As a solution for this issue, in this thesis, I considered a paradigm shift toward self-aware approaches and frameworks, which resulted in less energy consumption and longer battery lifetime, hence enabling ambulatory patient monitoring. I provided the possibility of switching between energy-efficient and high-performance modes in these systems. The second challenge is improving performance while guaranteeing the accuracy of the system. In this work, I have addressed this second challenge by combining multi-parametric bio-signals analysis and personalizing the detection for each patient. Throughout this thesis, I focused on epileptic seizure detection systems as a principal case study to analyze and evaluate the proposed techniques.