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.

A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients' vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss.