Regulatory authorities require that the safety and efficacy of a new high-risk medical device be proven in a Clinical Trial (CT), in which the effects of the device on a group of patients are compared to the effects of the current standard of care. Phase III trials can run for several years, cost millions of dollars, and expose patients to an unproven device. In this paper, we demonstrate how to use a large group of synthetic patients based on computer modeling to improve the planning of a CT so as to increase the chances of a successful trial for implantable cardioverter defibrillators (ICDs). We developed a computer model of the electrical generation and propagation in the heart. This model was used to generate a large group of heart instances capable of producing episodes of 19 different arrhythmias. We also implemented two arrhythmia detection algorithms from the literature: Rhythm ID from Boston Scientific and PR Logic + Wavelet from Medtronic. Using this setup, we conducted multiple in-silico trials to compare the ability of the two algorithms to appropriately discriminate between potentially fatal Ventricular Tachy-arrhythmias (VT) and nonfatal Supra-Ventricular Tachy-arrhythmias (SVTs). The results of our in-silico trial indicate that Rhythm ID was less able to discriminate between SVT and VT and so may lead to more cases of inappropriate therapy. This corroborates the findings of the Rhythm ID Going Head to Head Trial (RIGHT), a clinical trial that compared the two algorithms in patients. We further demonstrated that the result continues to hold if we vary the distribution of arrhythmias in the synthetic population. We also used the same in-silico cohort to explore the sensitivity of the outcome to different parameter settings of the device algorithms, which is not feasible in a real clinical trial. In-silico trials can provide early insight into the factors which affect the outcome of a CT at a fraction of the cost and duration and without the ethical issues.
An electrochemical based system with multiple layers coated on a functionalized graphene oxide Au electrode was developed to measure glucose concentration in urine in a more stable way. Two types of gold printed circuit boards were fabricated and graphene oxide was immobilized on their surface by chemical adsorption. Multiple layers, composed of a couple of polymers, were uniformly coated on the surface electrode. This device exhibited higher electrochemical responses against glucose, a greater resistivity in the presence of interferential substances in urine, and durable stabilities for longer periods of time than conventional units. The efficiency in current level according to the order and ratio of solution was evaluated during the immobilization of the layer. The fabricated electrodes were then also evaluated using hyperglycemic clinical samples and compared with the patterns of blood glucose measured with commercially available glucose meters. Our findings show that not only was their pattern similar but this similarity is well correlated.
We firstly demonstrated the detection of anions in air using a nano field-effect transistor (nanoFET) device. Negative ions in air charged the top surface of the silicon nanoFET channel affecting the fieldeffect and making a conductance change of the channel proportional to anion concentration around the nano channel sensing surface. The real-time detection of anions in air with the nanoFET was performed for various anion concentrations which were differentiated by the distance of the anion generator to the nanoFET sensor. The air anions detection characteristics of the nanoFET device were evaluated with sensitivity and conductance change rates analysis.
Low-power devices that can detect clinically relevant correlations in physiologically-complex patient signals can enable systems capable of closed-loop response (e.g., controlled actuation of therapeutic stimulators, continuous recording of disease states, etc.). In ultra-low-power platforms, however, hardware error sources are becoming increasingly limiting. In this paper, we present how data-driven methods, which allow us to accurately model physiological signals, also allow us to effectively model and overcome prominent hardware error sources with nearly no additional overhead. Two applications, EEG-based seizure detection and ECG-based arrhythmia-beat classification, are synthesized to a logic-gate implementation, and two prominent error sources are introduced: (1) SRAM bit-cell errors and (2) logic-gate switching errors ('stuck-at' faults). Using patient data from the CHB-MIT and MIT-BIH databases, performance similar to error-free hardware is achieved even for very high fault rates (up to 0.5 for SRAMs and 7 × 10(-2) for logic) that cause computational bit error rates as high as 50%.
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