Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer's disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epileptic patients continue to have seizures. More importantly, epilepsy-related causes of death account for 40% of mortality in high-risk patients. However, no reliable wearable device currently exists for real-time epileptic seizure detection. In this paper, we propose e-Glass, a wearable system based on four electroencephalogram (EEG) electrodes for the detection of epileptic seizures. Based on an early warning from e-Glass, it is possible to notify caregivers for rescue to avoid epilepsy-related death due to the underlying neurological disorders, sudden unexpected death in epilepsy, or accidents during seizures. We demonstrate the performance of our system using the Physionet.org CHB-MIT Scalp EEG database for epileptic children. Our experimental evaluation demonstrates that our system reaches a sensitivity of 93.80% and a specificity of 93.37%, allowing for 2.71 days of operation on a single battery charge.

Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things, it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA.

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Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to time and location. In this paper, we propose a self-aware wearable system for real-time detection of epileptic seizures on a long-term basis. First, we propose a multi-parametric machine learning technique to detect seizures by analyzing both cardiac and respiratory responses to seizures, which are obtained using only the ECG signal. Second, in order to enable long-time epilepsy detection, we introduce the notion of self-awareness in our real-time wearable system. We evaluate the performance of our proposed solution based on an epilepsy database of more than 211 hours of recording, provided by the Lausanne University Hospital (CHUV), on the INYU wearable sensor. Our proposed system achieves a sensitivity of 88.66% and a specificity of 85.65% before applying self-awareness. Moreover, by controlling the energy-quality trade-offs using our self-aware energy-management technique, we can tune the battery lifetime of the wearable system to last between 67.55 and 136.91 days while, still outperforming the state-of-the-art techniques for wearable seizure detection, by achieving from 85.54% to 79.33% geometric mean of specificity and sensitivity.

Abstract-Many embedded systems comprise several controllers sharing available resources. It is well known that such resource sharing leads to complex timing behavior that degrades the quality of control, and more importantly, can jeopardize stability in the worst-case, if not properly taken into account during design. Although stability of the control applications is absolutely essential, a design flow driven by the worst-case scenario often leads to poor control quality due to the significant amount of pessimism involved and the fact that the worst-case scenario occurs very rarely. On the other hand, designing the system merely based on control quality, determined by the expected (average-case) behavior, does not guarantee the stability of control applications in the worst-case. Therefore, both control quality and worst-case stability have to be considered during the design process, i.e., period assignment, task scheduling, and controlsynthesis. In this paper, we present an integrated approach for designing high-quality embedded control systems, while guaranteeing their stability.

Abstract-Guaranteeing stability of control applications in embedded systems, or cyber-physical systems, is perhaps the alpha and omega of implementing such applications. However, as opposed to the classical real-time systems where often the acceptance criterion is meeting the deadline, control applications do not primarily enforce hard deadlines. In the case of control applications, stability is considered to be the main design criterion and can be expressed in terms of the amount of delay and jitter a control application can tolerate before instability. Therefore, new design and analysis techniques are required for embedded control systems.In this paper, the analysis and design of such systems considering server-based resource reservation mechanism are addressed. The benefits of employing servers are manifold: (1) providing a compositional framework, (2) protection against other tasks misbehaviors, and (3) systematic bandwidth assignment. We propose a methodology for designing bandwidthefficient servers to stabilize control tasks.

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Abstract-Many cyber-physical systems comprise several control applications sharing communication and computation resources. The design of such systems requires special attention due to the complex timing behavior that can lead to poor control quality or even instability. The two main requirements of control applications are: (1) robustness and, in particular, stability and (2) high control quality. Although it is essential to guarantee stability and provide a certain degree of robustness even in the worst-case scenario, a design procedure which merely takes the worst-case scenario into consideration can lead to a poor expected (averagecase) control quality, since the design is solely tuned to a scenario that occurs very rarely. On the other hand, considering only the expected quality of control does not necessarily provide robustness and stability in the worst-case. Therefore, both the robustness and the expected control quality should be taken into account in the design process. This paper presents an efficient and integrated approach for designing high-quality cyberphysical systems with robustness guarantees.