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The interest of volumetric signals measured by respiratory inductive plethysmography is twofold: (1) clinical as their measurement is non-invasive and (2) physiological as they reveal the interactions between the cardiac and respiratory systems. Interestingly these signals may be seen as the addition of non-stationary cardiac and respiratory modes ordered in frequency. This feature makes them particularly suitable for time-frequency representation based on the pursuit of modes like Synchrosqueezing (SQ). As a preliminary to an application to real cardio-respiratory (CR) signals, a simulated approach has been chosen because of the availability of a reference signal which allows both the validation of the approach and the fine-tuning of the parameters of the algorithm used. Notably, the number of modes necessary to reconstruct the cardiac component from the decomposition of the whole CR signal was analyzed and might be limited to 6. So, this paper aims at investigating the functioning of SQ on one simulated CR signal and comparing its performance with the one of an already tried and tested approach: Empirical Mode Decomposition. In the present set-up, it turns out that the two approaches provide equivalent results. [ABSTRACT FROM PUBLISHER]

Monitoring circadian rhythms of social activity is crucial for preserving the health and wellness of ill elderly people. In this paper, we assess the ability of phones to be used as a temporal and social daily activity sensor from a passive and unobstructive measure of phone call activity. To this end, we introduce a methodology specifically designed to automatically measure both persistence and disruptions in circadian rhythms of phone call activity with 26 adults older than 65 years. [ABSTRACT FROM AUTHOR]

In the 5P medicine (Personalized, Preventive, Participative, Predictive and Pluri-expert), the general trend is to process data by displacing the barycenter of the information from hospital centered systems to the patient centered ones through his personal medical records. Today, the use of artificial intelligence for supporting this transition shows real limitations in its implementation in operational practice, both at the level of patient care, but also in the general daily life of the health professional, because of the medico-legal imperatives induced by the promises of the '5P medicine'. In this paper, we propose to fill this gap by introducing an original artificial intelligence platform, named Maxwell, which follows an unsupervised learning approach in line with the medico-legal imperatives of the ‘5P medicine’. We describe the functional platform characteristics and illustrate them by two examples of clustering in genomics and magnetic resonance imaging. [ABSTRACT FROM AUTHOR]