Kuk Jin Jang

A wearable healthcare system for cardiac signal monitoring using conductive textile electrodes

Accurate cardiac signal monitoring feasible for long-term monitoring is important for a practical, cost-effective health monitoring system. In this study, we propose a wearable healthcare system based on conductive fabric-based electrodes allowing monitoring of electrocardiogram (ECG) waveforms and demonstrated the potential for arrhythmia detection using the system. The measurement system uses conductive fabric-based electrodes arranged in a modified bipolar electrode configuration on the chest area of the patient. An adaptive impulse correlation filter (AICF) algorithm and a band pass filter to enable accurate R-peak detection in noisy environments.

Reusable urine glucose sensor based on functionalized graphene oxide conjugated Au electrode with protective layers

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.

The Challenges of High-Confidence Medical Device Software

Bringing new safety-critical medical devices to market faces several major challenges, but modeling and formal methods can facilitate this process from early system requirements verification to platform-level testing to late-stage clinical trials.

In-silico pre-clinical trials for implantable cardioverter defibrillators

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.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.

High-level modeling for computer-aided clinical trials of medical devices

Medical devices like the Implantable Cardioverter Defibrillator (ICD) are life-critical systems. Malfunctions of the device can cause serious injury or death of the patient. In addition to rigorous testing and verification during the development process, new medical devices often go through clinical trials to evaluate their safety and performance on sample populations. Clinical trials are costly and prone to failure if not planned and executed properly. Evaluating devices on computer models of the relevant physiological systems can provide helpful insights into the safety and efficacy of the device, thus helping to plan and execute a clinical trial. In this paper, we demonstrate how to develop high-level physiological models of cardiac electrophysiology and how to apply them to the Rhythm ID Head to Head Trial (RIGHT), a 5-year long clinical trial for comparing two ICDs. We refer to this as a Computer-Aided Clinical Trial (CACT). We explored two modeling options, a white-box model capturing the mechanisms of the physiological behaviors, and a blackbox model which uses machine learning methods to synthesize physiological input signals. Both models were able to generate physiological inputs to the ICDs and we discuss the challenges and appropriateness of the two modeling options.

Three challenges in cyber-physical systems

The tight coupling of computation, communication and control with physical systems such as actuation of closed-loop medical devices within the human body, peak power minimization by coordination of controllers across large industrial plants, and fast life-critical decision making by autonomous vehicles, present a set of fundamental and unique challenges. Each of these require new approaches at the intersection of multiple scientific, human and systems disciplines. We discuss five such challenges which require creative insights and application of model-based design, control systems, scheduling theory, formal methods, statistical machine learning and domain-specific experimentation. We ask the following questions: (1) An autonomous medical device is implanted to control your heart over a period of 5-7 years. How do you guarantee the software in the device provides safe and effective treatment under all physiological conditions? (2) Electricity prices in the US have summer peaks that are over 32× their average prices and winter peaks that are 86×. How can buildings respond to massive swings in energy prices at fast time scales? (3) While wireless has been successfully used for open-loop monitoring and tracking, how can we operate closed-loop control systems over a network of wireless controllers. Furthermore, how can we ensure robust, optimal and secure control in the presence of node/link failures and topology changes?

Multiple reaction analysis of cancer with different markers using silicon nanowire FET

In this study, we have used newly developed Silicon nanowire (SiNW) arrays to evaluate their feasibility for the quantification of different markers of interests. We have quantified four different markers of PSA, EGF, IL-6, and VDBP. Each marker showed measurements in the range of 0.184 ~ 17.79 ng/mL (PSA), 10 pg/mL ~ 10 ng/mL (EGF), 10 pg/mL ~ 50 ng/mL (IL-6), and 10 pg ~ 5 ng/mL (VDBP), respectively. For the experiment, we collected 10 different serum samples, 5 prostate cancer patients and 5 breast cancer patients, and measured and compared the resulting signal from the SiNW FET to serum sample from normal patients. As a result, we observed a meaningful pattern of markers associated with each type of cancer. In addition, we have measured the response signal of SiNWs conjugated with Epithelial cell adhesion molecules (EpCAM) markers against tumor cells as they interacted with those markers.