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Due to the rapid development of semiconductor technology, the number of transistors of integrated circuits in unit area increases by double in roughly every two years. We then can add more circuits and functionality into a single chip. The size of electronic products certainly is reduced. Besides, because of the blooming popularity of wireless network standards in recently year, sensors have been wireless connected to provide more functionality and intelligence. They are, namely, wireless sensor network (WSN). Before long, the integrated circuit design will not only be emphasized on front-end circuits and hardware design, but also integration and functionality, which is so-called the system-on-chip (SOC) design. The first topic of this thesis is the implementation of IEEE 802.15.4 network prototype and hardware design. The main purpose of prototyping is to realize the highly portable IEEE 802.15.4 protocol stack library which can be quickly transferred to different hardwares. Thus, it shortens the time to market. In ASIC hardware design, we use WISHBONE bus as the interconnection architecture which can be easily integrated into current SOC design for an embedded system. The second topic is an application of IEEE 802.15.4 in medical monitoring, including system prototyping and ASIC hardware design, which collects the bladder pressure readings by a wireless link and ECG signals from our ASIC sensors. Finally, we realize the medical monitoring in a prototypical system.

Afin d'accompagner les professionnels de santé dans leur démarche clinique, plusieurs systèmes de suivi et deprise en charge médicale ont été construits et déployés dans le milieu hospitalier. Ces systèmes permettentprincipalement de collecter des données médicales sur les patients, de les analyser et de présenter les résultats dedifférentes manières. Ils représentent un appui et une aide aux professionnels de santé dans leur prise de décisionpar rapport à l'évolution de l'état de santé des patients suivis. L'utilisation de tels systèmes nécessitesystématiquement une adaptation à la fois au domaine médical concerné et au mode d'intervention. Il estnécessaire, dans un milieu hospitalier, que ces systèmes puissent s'adapter et évoluer d'une manière simple, enlimitant toute maintenance corrective ou évolutive. Ils doivent être en mesure de prendre en compte dynamiquementdes connaissances théoriques et empiriques du domaine issues des experts médicaux.Afin de répondre à ces exigences, nous avons proposé une approche pour la construction d'un système d'aide à ladécision médicale capable de s'adapter au domaine médical concerné et au mode d'intervention approprié pourassister les professionnels de santé dans leur démarche clinique. Cette approche permet notamment l'organisationde la collecte des données médicales, en tenant compte du contexte du patient, la représentation desconnaissances du domaine à base d'ontologies ainsi que leur exploitation associée aux guides de bonnes pratiqueset à l'expérience clinique.Dans la continuité des travaux précédemment réalisés au sein de notre équipe de recherche, nous avons choisid'enrichir, avec notre approche, la plateforme E-care qui est dédiée au suivi et à la détection précoce de touteanomalie de l'état de patients atteints de maladies chroniques. Nous avons pu ainsi adapter facilement la plateformeE-care aux différentes expérimentations qui sont été menées notamment dans des EPHAD de la MutualitéFrançaise en Anjou-Mayenne, au CHU de Hautepierre et au CHUV à Lausanne.Les résultats de ces expérimentations ont montré l'efficacité de l'approche proposée. L'adaptation de la plateformepar rapport au domaine et au mode d'intervention de chacune de ces expérimentations se limite à de la simpleconfiguration. De plus, l'approche proposée a suscité l'intérêt du personnel médical par rapport à l'organisation de lacollecte des données, qui tient compte du contexte du patient, et par rapport à l'exploitation des connaissancesmédicales qui apporte aux professionnels de santé une assistance pour une meilleure prise de décision. / To support health professionals in their clinical processes, several monitoring and medical care systems have beenbuilt and deployed in the hospital setting. These systems are mainly used to collect medical data on patients,analyze and present the outcomes in different ways. They represent support and assistance to health professionalsin their decision making regarding the evolution in the health status of the patients followed. The use of suchsystems always requires an adaptation to both the medical field and the mode of intervention. It is necessary, in ahospital setting, to adapt and evolve these systems in a simple manner, limiting any corrective or evolutionarymaintenance. Moreover, these systems should be able to consider dynamically the domain knowledge from medicalexperts.To meet these requirements, we proposed an approach for the construction of a medical decision support system(MDSS). This MDSS can adapt to the medical field and to the appropriate mode of intervention to assist healthprofessionals in their clinical processes. This approach allows especially the organization of the medical datacollection by taking into account the patient¿s context, the ontology-based knowledge representation of the domainand permits the exploitation of the medical guidelines and the clinical experience.In continuity of our research team¿s previous work, we chose to expand with our approach, the E-care platformwhich is dedicated to monitoring and early detection of any abnormality of the health status of patients with chronicdiseases. We were able to adapt easily the E-care platform for the various experiments that have been conducted,including EPHAD of the Mutualité Française in Anjou-Mayenne, Hautepierre hospital and Lausanne hospital(CHUV).The outcomes of these experiments have shown the effectiveness of the proposed approach. Where, the adaptationof the platform regarding to the domain and mode of intervention of each of these experiments is limited to thesimple configuration. Furthermore, the proposed approach has attracted the interest of the medical staff regardingthe organization of the medical data collection, and the exploitation of the medical knowledge which bringsassistance to the health professionals for better decision making.

The research in this thesis has been undertaken to develop statistical tools for monitoring adverse events in hospitals that adjust for varying patient risk. The studies involved a detailed literature review of risk adjustment scores for patient mortality following cardiac surgery, comparison of institutional performance, the performance of risk adjusted CUSUM schemes for varying risk profiles of the populations being monitored, the effects of uncertainty in the estimates of expected probabilities of mortality on performance of risk adjusted CUSUM schemes, and the instability of the estimated average run lengths of risk adjusted CUSUM schemes found using the Markov chain approach. The literature review of cardiac surgical risk found that the number of risk factors in a risk model and its discriminating ability were independent, the risk factors could be classified into their "dimensions of risk", and a risk score could not be generalized to populations remote from its developmental database if accurate predictions of patients' probabilities of mortality were required. The conclusions were that an institution could use an "off the shelf" risk score, provided it was recalibrated, or it could construct a customized risk score with risk factors that provide at least one measure for each dimension of risk. The use of report cards to publish adverse outcomes as a tool for quality improvement has been criticized in the medical literature. An analysis of the report cards for cardiac surgery in New York State showed that the institutions' outcome rates appeared overdispersed compared to the model used to construct confidence intervals, and the uncertainty associated with the estimation of institutions' out come rates could be mitigated with trend analysis. A second analysis of the mortality of patients admitted to coronary care units demonstrated the use of notched box plots, fixed and random effect models, and risk adjusted CUSUM schemes as tools to identify outlying hospitals. An important finding from the literature review was that the primary reason for publication of outcomes is to ensure that health care institutions are accountable for the services they provide. A detailed review of the risk adjusted CUSUM scheme was undertaken and the use of average run lengths (ARLs) to assess the scheme, as the risk profile of the population being monitored changes, was justified. The ARLs for in-control and out-of-control processes were found to increase markedly as the average outcome rate of the patient population decreased towards zero. A modification of the risk adjusted CUSUM scheme, where the step size for in-control to out-of-control outcome probabilities were constrained to no less than 0.05, was proposed. The ARLs of this "minimum effect" CUSUM scheme were found to be stable. The previous assessment of the risk adjusted CUSUM scheme assumed that the predicted probability of a patient's mortality is known. A study of its performance, where the estimates of the expected probability of patient mortality were uncertain, showed that uncertainty at the patient level did not affect the performance of the CUSUM schemes, provided that the risk score was well calibrated. Uncertainty in the calibration of the risk model appeared to cause considerable variation in the ARL performance measures. The ARLs of the risk adjusted CUSUM schemes were approximated using simulation because the approximation method using the Markov chain property of CUSUMs, as proposed by Steiner et al. (2000), gave unstable results. The cause of the instability was the method of computing the Markov chain transition probabilities, where probability is concentrated at the midpoint of its Markov state. If probability was assumed to be uniformly distributed over each Markov state, the ARLs were stabilized, provided that the scores for the patients' risk of adverse outcomes were discrete and finite.

Heart rate, Oxygen saturation, HR, SpO2, MATLAB, smartphone, mobile phone, photopletysmogram, PPG