Ballistocardiographie et applications

Résumé fourni par la source

Globally, healthcare systems have increasing costs and the number of hospitalizations grows. Telehealth brings hospital at home and provides health structures with new opportunities to improve the patient care pathway. Physiological monitoring is a prerequisite in efficient telehealth systems and is performed by connected medical devices that are not fully automated. Patients need to use them actively on a day-to-day basis: these drawbacks lead either to patient disengagement or to additional caregiver support. Passive contactless vital signs’ monitors, such as ballistocardiograms sleep trackers that measure motor, respiratory and cardiac activities, can solve the telehealth inefficiency. Moreover, they are more comfortable and safer for patients than traditional monitors, which is crucial for neonatal neurological development or in case of mental degeneration, though they are currently less accurate. How to improve physiological monitoring accuracy in ballistocardiography to increase telehealth efficiency? In this thesis, materials are provided by a self-designed accelerometer-based instrumentation, a dedicated software, a heartbeat simulator, and measurement campaigns for raw ballistocardiograms’ databases. Novel analog amplification and digital filtering methods are investigated to improve ballistocardiography accuracy. The ballistocardiographic force, coming from the aortic arch deformation during the ventricular systole and measured on the bedside, is indeed modulated by respiratory and motor activities, and is polluted by environment mechanical artifacts. Furthermore, the ballistocardiography is unstandardized and ballistocardiograms have high inter- and intra-variabilities, depending on the beddings, the position in bed, the morphology and the physiology of the patient. Analog amplification is studied from two perspectives: the mechanical amplification of ballistocardiograms from the patient to the sensor, and the electronic amplification of the analog acceleration signal. First, concerning the mechanical amplification, a novel waveguide bedding, a cotton tape encircling the mattress, was invented to concentrate the strain energy of the ballistocardiographic force in one direction, from the thorax straight to the attached sensor. Second, concerning the electronic amplification, a mixed-signal front-end was conceived to optimize the tradeoff between the electronic amplifier gain and the saturation time after a movement. The conditioning circuit measures the unamplified sensor output, passes it through a digital filter with a sharp transition frequency bandwidth and a proper initialization, and analogically amplifies the difference between this unwanted synthesized signal and the unamplified sensor output using a low noise instrumentation amplifier. Digital filtering methods aims at separating signal sources, removing artifacts then detecting vital signs. Three original algorithms have been designed to efficiently recognize heartbeats in ballistocardiograms. The first algorithm is dynamic time warping template matching, where a heartbeat template is used to match heartbeats using a warping distance. The second algorithm models ballistocardiograms with periodic hidden Markov models. The third algorithm, the U-Net neural network, is supervised and segments heartbeats in ballistocardiograms. Finally, ballistocardiograms are mechanically and electronically amplified by 12 dB and 21 dB respectively, without saturation time; and digital filtering algorithms reach a 97% precision and 96% recall for heartbeats detection. Shortly, the designed ballistocardiograph will be clinically evaluated in a pediatric intensive care unit and in telemedicine against other ballistocardiographs and the gold standard methods.

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