Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Development of a computer-assisted high-pressure Injection device for vertebroplasty

A novel computer-assisted injection device for the delivery of highly viscous bone cements in vertebroplasty is presented. It addresses the shortcomings of manual injection systems ranging from low-pressure and poor level of control to device failure. The presented instrument is capable of generating a maximum pressure of 5000 kPa in traditional 6-ml syringes and provides an advanced control interface for precise cement delivery from outside radiation fields emitted by intraoperative imaging systems. The integrated real-time monitoring of injection parameters, such as flow-rate, volume, pressure, and viscosity, simplifies consistent documentation of interventions and establishes a basis for the identification of safe injection protocols on the longer term. Control algorithms prevent device failure due to overloading and provide means to immediately stop cement flow to avoid leakage into adjacent tissues.

Hop-to-Hop Reliability in IP-Based Wireless Sensor Networks - A Cross-Layer Approach

To interconnect a wireless sensor network (WSN) to the Internet, we propose to use TCP/IP as the standard protocol for all network entities. We present a cross layer designed communication architecture, which contains a MAC protocol, IP, a new protocol called Hop-to-Hop Reliability (H2HR) protocol, and the TCP Support for Sensor Nodes (TSS) protocol. The MAC protocol implements the MAC layer of beacon-less personal area networks (PANs) as defined in IEEE 802.15.4. H2HR implements hop-to-hop reliability mechanisms. Two acknowledgment mechanisms, explicit and implicit ACK are supported. TSS optimizes using TCP in WSNs by implementing local retransmission of TCP data packets, local TCP ACK regeneration, aggressive TCP ACK recovery, congestion and flow control algorithms. We show that H2HR increases the performance of UDP, TCP, and RMST in WSNs significantly. The throughput is increased and the packet loss ratio is decreased. As a result, WSNs can be operated and managed using TCP/IP.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Red blood cell lifespan, erythropoiesis and hemoglobin control

Erythropoietin (EPO) and iron deficiency as causes of anemia in patients with limited renal function or end-stage renal disease are well addressed. The concomitant impairment of red blood cell (RBC) survival has been largely neglected. Properties of the uremic environment like inflammation, increased oxidative stress and uremic toxins seem to be responsible for the premature changes in RBC membrane and cytoskeleton. The exposure of antigenic sites and breakdown of the phosphatidylserine asymmetry promote RBC phagocytosis. While the individual response to treatment with EPO-stimulating agents (ESA) depends on both the RBC's lifespan and the production rate, uniform dosing algorithms do not meet that demand. The clinical use of mathematical models predicting ESA-induced changes in hematocrit might be greatly improved once independent estimates of RBC production rate and/or lifespan become available, thus making the concomitant estimation of both parameters unnecessary. Since heme breakdown by the hemoxygenase pathway results in carbon monoxide (CO) which is exhaled, a simple CO breath test has been used to calculate hemoglobin turnover and therefore RBC survival and lifespan. Future research will have to be done to validate and implement this method in patients with kidney failure. This will result in new insights into RBC kinetics in renal patients. Eventually, these findings are expected to improve our understanding of the hemoglobin variability in response to ESA.

[Optimised glycaemic control and dose titration with insulin glargine (Lantus) by general practitioners in Switzerland: results of the OPTI-LAN Postmarketing Study]

National and international societies have published guidelines regarding glycaemic control in type-2 diabetes mellitus. Clinical studies have shown that glycaemic control of type-2 diabetes mellitus can be improved using simple algorithms for titration of insulin Glargine (Lantus). It is unclear, to what degree published guidelines are adopted in daily practice in Switzerland.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

Towards End-User Driven Power Saving Control in Android devices

Abstract. During the last decade mobile communications increasingly became part of people's daily routine. Such usage raises new challenges regarding devices' battery lifetime management when using most popular wireless access technologies, such as IEEE 802.11. This paper investigates the energy/delay trade-off of using an end-user driven power saving approach, when compared with the standard IEEE 802.11 power saving algorithms. The assessment was conducted in a real testbed using an Android mobile phone and high-precision energy measurement hardware. The results show clear energy benefits of employing user-driven power saving techniques, when compared with other standard approaches.