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.

Monitoring patients in intensive care units is generally expensive and time consuming. A prompter medical intervention for those critical patients is a key factor for their safety. Therefore, a system that offers immediate visualization of the monitored data represents a great advance in the field. In this paper, the design, the development, and the validation of an android interface for the continuous and wireless monitoring of up to five compounds are described. Continuous monitoring of the biomolecules is addressed by using a fully integrated hardware platform consisting of biosensors connected to a read-out circuit on a printed circuit board. The electrochemical platform uses Roving's Bluetooth module RN-42 to send the measured data to the mobile device. For the validation of the system, some biomolecules are taken as reference: glucose and lactate for endogenous metabolites and paracetamol for exogenous biomolecules. Chronoamperometries are performed at +650 mV for glucose and lactate and at +450 mV for paracetamol. Multi-walled carbon nanotubes are deposited on working electrodes for glucose and lactate for enhanced signal. Instead, for paracetamol, bare working electrodes are used. The measured data are continuously displayed on the screen of the mobile device because of the android interface. Current step for every variation of glucose, lactate, or paracetamol is clearly visible by the trend of the graphs.

In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device’s battery lifetime. However, energy savings are possible when relying on an event-triggered strategy, following the brain example. In this paper, we explore the use of non-Nyquist sampling for cardiovascular monitoring systems, with an in-depth analysis of the performance of a knowledge-based adaptive sampling strategy. By reducing the average sampling rate from 360 Hz down to 13.6 Hz, we can increase the battery lifetime by 4x with a marginal impact on the accuracy of heart-rate analysis.

Obstructive Sleep Apnea (OSA) is one of the main sleep disorders, but only 10% of the cases are diagnosed. Moreover, there is a lack of tools for long-term monitoring of OSA, since current systems are too bulky and intrusive to be used continuously. In this context, recent studies have shown that it is possible to detect it automatically based on single-lead ECG recordings. This approach can be used in non-invasive smart wearable sensors which measure and process bio-signals online. This work focuses on the implementation, optimization and integration of an algorithm for OSA detection for preventive health-care. It relies on a frequency-domain analysis while targeting an ultra-low power embedded wearable device. As it must share its resources usage with other computations, it must be as lightweight as possible. Our current results based on publicly available signals show a classification accuracy of up to 83.2% for both the offline analysis and the embedded online one. This system gives an even better classification accuracy than the best offline algorithm when using the same features for classification [1].

Latest progress in microelectronics have enabled a new generation of low cost, low power, miniaturized, yet, smart sensor nodes. This new generation of wearable sensor nodes promise to deploy automated complex bio-signals analysis. In this paper, we present INYU, a wearable sensor device for physical and emotional health monitoring. The device obtains key vital signs of the user, namely Electrocardiogram (ECG), respiration and skin conductance continuously. Using this information, INYU can deliver a novel real-time algorithm for on-line heart-beat classification and correction that relies on a probabilistic model to determine whether a heartbeat is likely to happen under certain timing conditions. Thus, using this algorithm INYU can quickly decide if a beat is occurring at an expected time or if there is a problem in the series (e.g., a skipped, an extra or a misplaced beat). This new algorithm has been integrated in the processing pipeline of automated Heart-Rate Variability (HRV) analysis, both for time-domain (RMSSD, SDNN) and frequencydomain (LF/HF) algorithms.

A full remotely powered system for metabolism monitoring of free-moving mice is presented here. The fully implantable sensing platform hosts two ASICs, one off-the-shelf micro-controller, four biosensors, two other sensors, a coil to receive power, and an antenna to transmit data. Proper enzymes ensure specificity for animal metabolites while Multi-Walled Carbon Nanotubes ensure the due sensitivity. The remote powering is indeed provided by inductive coils located under the floor of the mouse' cage. Two different approaches where investigated to ensure freedom of movement to the animal. The application to studies for personalized medicine is demonstrated by showing continuous monitoring of both glucose and paracetamol.