Hyeokjun Kwon

Wireless Point-of-Care Diagnosis for Sleep Disorder With Dry Nanowire Electrodes

Currently, available sleep monitoring systems use electrical recording where the electrodes make contact with the patient’s skin using a conducting gel. The electrode wires are connected to a processing recording system. The subject has to be in close proximity of these machines due to the direct electrical connections with the body and the machine. The conductive gel along with many wires connected to the biopotential electrodes makes them uncomfortable for the subject, with the result that recording and monitoring of the patient’s sleep patterns can become very difficult. The patient has to be in a sleep lab and/or a hospital at all times and at least one technician needs to watch the patient’s sleep behavior via video. The patient may not experience normal sleep patterns under such environments and as such, the diagnostic results are not really very conclusive. The commonly monitored biopotential electrodes are electrocardiogram, electroencephalogram, electromyogram, and electrooculogram. The electrodes used for monitoring these signals are Ag/AgCl and gold, which require skin preparation by means of scrubbing to remove the dead cells and application of electrolytic gel to reduce the skin contact resistance. The gel takes a role of reducing skin contact impedance in the conventional Ag/AgCl electrode and its usage is directly related to the sensitivity. However, the wet conventional Ag/AgCl electrode has some drawbacks such as difficulty in long time monitoring because the gel dries out after few hours and skin irritations. Usually, physiological parameters are monitored over an extended period of time during the patient’s normal daily life to diagnose a disease. In this case, the wet conventional Ag/AgCl cannot be used because of the dry-out of gel. The dry-out of gel increases the impedance between skin and electrode and it is reflected in the poor signal sensitivity. Also noises, such as motion artifact and baseline wander, are added to the biopotential signals as the electrode floats over the electrolytic gel during monitoring. To overcome these drawbacks, dry nanoelectrodes are proposed in this paper where the electrodes are held against the skin surface to establish contact with the skin without the need for electrolytic fluids or gels. The results are presented along with a wireless communication such that the proposed system is ideal for point-of-care diagnosis of the patient at home.

e-Nanoflex Sensor System: Smartphone-Based Roaming Health Monitor

The growing need and market demand for point of care (POC) systems to improve patient’s quality of life are driving the development of wireless nanotechnology based smart systems for diagnosis and treatment of various chronic and life threatening diseases. POC diagnostics for neurological, metabolic, and cardiovascular disorders require constant long term untethered monitoring of individuals. Given the uncertainty associated with location and time at which immediate diagnosis and treatment may be required, constant vigilance and monitoring are the only practical solutions. What is needed is for a remote cyber-enabled health care smart system incorporating novel ideas from nanotechnology, low power embedded systems, wireless networking, and cloud computing to fundamentally advance. To meet this goal, we present e-Nanoflex platform, which is capable of monitoring patient health wherever they may be and communicating the data in real time to a physician or a hospital. Unlike state-of-the-art systems that are either local sensor systems or rely on custom relaying devices, e-Nanoflex is a highly nonintrusive and inexpensive end-to-end cyber-physical system. Using nanostructured sensors, e-Nanoflex provides nearly invisible monitoring of physiological conditions. It relies on smartphones to filter, compress, and relay geo-tagged data. Further, it ties to a backend cloud infrastructure for data storage, data dissemination, and abnormality detection using machine learning techniques. e-Nanoflex is a complete end-to-end system for physiological sensing and geo-tagged data dissemination to hospitals and caregivers. It is intended as a basic platform that can support any nanostructure based flexible sensor to monitor a variety of conditions such as body temperature, respiration air flow, oxygen consumption, bioelectric signals, pulse oximetry, muscle activity, and neural activity. Additionally, to address the cost of manufacturing sensors, e-Nanoflex uses a low cost production technique based on roll to roll gravure printing. We show the efficacy of our platform through a case study that involves acquiring electrocardiogram signals using gold nano-electrodes fabricated on a flexible substrate.

Wireless Health Monitoring Helmet for Football Players to Diagnose Concussion and Track Fatigue

Football players are regularly exposed to violent impacts. Concussions are mild traumatic brain injuries that are one of the most common injuries experienced by football players. These concussions are often overlooked by football players themselves and the clinical criteria used to diagnose them. The cumulative effect of these mild traumatic brain injuries can cause long-term residual brain dysfunctions. In addition, an athlete’s fatigue level should be monitored to prevent any secondary injuries due to over exertion. Nitric Oxide acts as a metabolic adjustment factor that controls the flow of oxygen in blood and the contraction/relaxation of muscles. Fatigue can be evaluated by measuring the concentration change of nitric oxide in blood. However, measuring the concentration of nitric oxide in blood is not feasible during exercise. Nevertheless, the degree of fatigue can be measured with SpO2 during exercise because the change of nitric oxide also influences the SpO2. In this paper, we propose a wireless health monitoring helmet to diagnose concussions and evaluate fatigue in real time and on the field. The helmet is equipped with sensors and a transmitter module. As sensors, textile based electrodes are used to sense EEG and oximeter sensors are used to derive SpO2. The sensed physiological signals are amplified and processed in the transmitter module. The processed signals are transmitted to a server using Zigbee wireless communication. The EEG signals are classified to diagnose concussion or any abnormality of brain function. In conclusion, the system can monitor and diagnose concussions and evaluate fatigue in football players in real time by measuring their EEGs and SpO2.

Smart healthcare textile sensor system for unhindered-pervasivehealth monitoring

Simultaneous monitoring of physiological parameters- multi-lead Electrocardiograph (ECG), Heart rate variability, and blood pressure- is imperative to all forms of medical treatments. Using an array of signal recording devices imply that the patient will have to be confined to a bed. Textiles offer durable platform for embedded sensor and communication systems. The smart healthcare textile, presented here, is a mobile system for remote/wireless data recording and conditioning. The wireless textile system has been designed to monitor a patient in a non-obstructive way. It has a potential for facilitating point of care medicine and streamlining ambulatory medicine. The sensor systems were designed and fabricated with textile based components for easy integration on textile platform. An innovative plethysmographic blood pressure monitoring system was designed and tested as an alternative to inflatable blood pressure sphygmomanometer. Flexible dry electrodes technology was implemented for ECG. The sensor systems were tested and conditioned to daily activities of patients, which is not permissible with halter type systems. The signal quality was assessed for it applicability to medical diagnosis. The results were used to corroborate smart textile sensor systems ability to function as a point of care system that can provide quality healthcare.

Nanocomposite electrodes for smartphone enabled healthcare garments: e-bra and smart vest

The financial burden of hospital readmissions and treatment of chronic cardiac diseases are global concerns. Point of Care (POC) has been presented as an elegant solution for healthcare cost reduction. However, large scale adoption of POC systems requires an intuitive, unobtrusive and easy to use health monitoring system from patient’s perspective. Healthcare textiles are sensor systems mounted on textile platform that function as wearable unobtrusive health monitoring systems. Although much work has been done in the development and demonstration of textile mounted monitoring systems, material and production costs are still high. Nanomaterials based devices and technology can be employed in these healthcare textiles for improved electrical characteristics of the sensors, lowered cost due to less material consumption and compatibility to varied manufacturing techniques. Carbon nanotube composite ink based printable conductive electrodes is such a textile adaptable nanomaterial technology. Screen printed Nanocomposite electrodes made of carbon nanotubes and an acrylic polymer can be used in undergarments like vests and brassieres, for cardiac biopotential (Electrocardiography, ECG) sensing. A Bluetooth module and a smartphone can then be used to provide cyber-infrastructure connectivity for the healthcare data from these healthcare garments. They can be used to monitor young or elderly recuperating /convalescent patients either in hospital or at home, or they can be used by young athletes to monitor important physiological parameters to better design their training or fitness program. In this study, we evaluate screen printed CNT-acrylic Nanocomposite electrodes for ECG signal quality and any CNT leaching hazard that might lead to skin toxicity.

Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

A brain-machine interface (BMI) links a users brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

Smart real-time cardiac diagnostic sensor systems for football players and soldiers under intense physical training

Sudden cardiac death (SCD) and acute myocardial infarctions (AMIs) have been reported to be up to 7.6 times higher in rate of occurrence during intense exercise as compared to sedentary activities. The risk is high in individuals with both diagnosed as well as occult heart diseases. Recently, SCDs have been reported with a high rate of occurrence among young athletes and soldiers who routinely undergo vigorous training. Prescreening Electrocardiograms (ECG) and echocardiograms have been suggested as potential means of detecting any cardiac abnormalities prior to intense training to avoid the risk of SCDs, but the benefits of this approach are widely debated. Moreover, the increased risk of SCDs and AMIs during training or exercise suggests that ECGs are of much greater value when acquired real-time during the actual training. The availability of immediate diagnostic data will greatly reduce the time taken to administer the appropriate resuscitation. Important factors to consider in the implementation of this solution are: - cost of overall system, accuracy of signals acquired and unobtrusive design. In this paper, we evaluate a system using printed sensors made of inks with functional properties to acquire ECGs of athletes and soldiers during physical training and basic military training respectively. Using Zigbee, we show that athletes and soldiers can be monitored in real time, simultaneously.

Printable low-cost sensor systems for healthcare smart textiles

Smart textiles-based wearable health monitoring systems (ST-HMS) have been presented as elegant solutions to the requirements of individuals across a wide range of ages. They can be used to monitor young or elderly recuperating /convalescent patients either in hospital or at home, or they can be used by young athletes to monitor important physiological parameters to better design their training or fitness program. Business and academic interests, all over the world, have fueled a great deal of work in the development of this technology since 1990. However, two important impediments to the development of ST-HMS are:-integration of flexible electrodes, flexible sensors, signal conditioning circuits and data logging or wireless transmission devices into a seamless garment and a means to mass manufacture the same, while keeping the costs low. Roll-to-roll printing and screen printing are two low cost methods for large scale manufacturing on flexible substrates and can be extended to textiles as well. These two methods are, currently, best suited for planar structures. The sensors, integrated with wireless telemetry, facilitate development of a ST-HMS that allows for unobtrusive health monitoring. In this paper, we present our results with planar screen printable sensors based on conductive inks which can be used to monitor EKG, abdominal respiration effort, blood pressure, pulse rate and body temperature. The sensor systems were calibrated, and tested for sensitivity, reliability and robustness to ensure reuse after washing cycles.

Wireless remote monitoring system for sleep apnea

Sleep plays the important role of rejuvenating the body, especially the central nervous system. However, more than thirty million people suffer from sleep disorders and sleep deprivation. That can cause serious health consequences by increasing the risk of hypertension, diabetes, heart attack and so on. Apart from the physical health risk, sleep disorders can lead to social problems when sleep disorders are not diagnosed and treated. Currently, sleep disorders are diagnosed through sleep study in a sleep laboratory overnight. This involves large expenses in addition to the inconvenience of overnight hospitalization and disruption of daily life activities. Although some systems provide home based diagnosis, most of systems record the sleep data in a memory card, the patient has to face the inconvenience of sending the memory card to a doctor for diagnosis. To solve the problem, we propose a wireless sensor system for sleep apnea, which enables remote monitoring while the patient is at home. The system has 5 channels to measure ECG, Nasal airflow, body position, abdominal/chest efforts and oxygen saturation. A wireless transmitter unit transmits signals with Zigbee and a receiver unit which has two RF modules, Zigbee and Wi-Fi, receives signals from the transmitter unit and retransmits signals to the remote monitoring system with Zigbee and Wi-Fi, respectively. By using both Zigbee and Wi-Fi, the wireless sensor system can achieve a low power consumption and wide range coverage. The systems features are presented, as well as continuous monitoring results of vital signals.

Motion artifact removal algorithm by ICA for e-bra: a women ECG measurement system

Wearable ECG(ElectroCardioGram) measurement systems have increasingly been developing for people who suffer from CVD(CardioVascular Disease) and have very active lifestyles. Especially, in the case of female CVD patients, several abnormal CVD symptoms are accompanied with CVDs. Therefore, monitoring women’s ECG signal is a significant diagnostic method to prevent from sudden heart attack. The E-bra ECG measurement system from our previous work provides more convenient option for women than Holter monitor system. The e-bra system was developed with a motion artifact removal algorithm by using an adaptive filter with LMS(least mean square) and a wandering noise baseline detection algorithm. In this paper, ICA(independent component analysis) algorithms are suggested to remove motion artifact factor for the e-bra system. Firstly, the ICA algorithms are developed with two kinds of statistical theories: Kurtosis, Endropy and evaluated by performing simulations with a ECG signal created by sgolayfilt function of MATLAB, a noise signal including 0.4Hz, 1.1Hz and 1.9Hz, and a weighed vector W estimated by kurtosis or entropy. A correlation value is shown as the degree of similarity between the created ECG signal and the estimated new ECG signal. In the real time E-Bra system, two pseudo signals are extracted by multiplying with a random weighted vector W, the measured ECG signal from E-bra system, and the noise component signal by noise extraction algorithm from our previous work. The suggested ICA algorithm basing on kurtosis or entropy is used to estimate the new ECG signal Y without noise component.