Hong Ji Lee

Conductive Polymer Foam Surface Improves the Performance of a Capacitive EEG Electrode

In this paper, a new conductive polymer foam-surfaced electrode was proposed for use as a capacitive EEG electrode for nonintrusive EEG measurements in out-of-hospital environments. The current capacitive electrode has a rigid surface that produces an undefined contact area due to its stiffness, which renders it unable to conform to head curvature and locally isolates hairs between the electrode surface and scalp skin, making EEG measurement through hair difficult. In order to overcome this issue, a conductive polymer foam was applied to the capacitive electrode surface to provide a cushioning effect. This enabled EEG measurement through hair without any conductive contact with bare scalp skin. Experimental results showed that the new electrode provided lower electrode-skin impedance and higher voltage gains, signal-to-noise ratios, signal-to-error ratios, and correlation coefficients between EEGs measured by capacitive and conventional resistive methods compared to a conventional capacitive electrode. In addition, the new electrode could measure EEG signals, while the conventional capacitive electrode could not. We expect that the new electrode presented here can be easily installed in a hat or helmet to create a nonintrusive wearable EEG apparatus that does not make users look strange for real-world EEG applications.

Estimation of Body Postures on Bed Using Unconstrained ECG Measurements

We developed and tested a system for estimating body postures on a bed using unconstrained measurements of electrocardiogram (ECG) signals using 12 capacitively coupled electrodes and a conductive textile sheet. Thirteen healthy subjects participated in the experiment. After detecting the channels in contact with the body among the 12 electrodes, the features were extracted on the basis of the morphology of the QRS (Q wave, R wave, and S wave of ECG) complex using three main steps. The features were applied to linear discriminant analysis, support vector machines with linear and radial basis function (RBF) kernels, and artificial neural networks (one and two layers), respectively. SVM with RBF kernel had the highest performance with an accuracy of 98.4% for estimation of four body postures on the bed: supine, right lateral, prone, and left lateral. Overall, although ECG data were obtained from few sensors in an unconstrained manner, the performance was better than the results that have been reported to date. The developed system and algorithm can be applied to the obstructive apnea detection and analyses of sleep quality or sleep stages, as well as body posture detection for the management of bedsores.

Capacitive measurement of ECG for ubiquitous healthcare.

The technology for measuring ECG using capacitive electrodes and its applications are reviewed. Capacitive electrodes are built with a high-input-impedance preamplifier and a shield on their rear side. Guarding and driving ground are used to reduce noise. An analysis of the intrinsic noise shows that the thermal noise caused by the resistance in the preamplifier is the dominant factor of the intrinsic noise. A fully non-contact capacitive measurement has been developed using capacitive grounding and applied to a non-intrusive ECG measurement in daily life. Many ongoing studies are examining how to enhance the quality and ease of applying electrodes, thus extending their applications in ubiquitous healthcare from attached-on-object measurements to wearable or EEG measurements.

Heart Rate Variability Monitoring during Sleep Based on Capacitively Coupled Textile Electrodes on a Bed

In this study, we developed and tested a capacitively coupled electrocardiogram (ECG) measurement system using conductive textiles on a bed, for long-term healthcare monitoring. The system, which was designed to measure ECG in a bed with no constraints of sleep position and posture, included a foam layer to increase the contact region with the curvature of the body and a cover to ensure durability and easy installation. Nine healthy subjects participated in the experiment during polysomnography (PSG), and the heart rate (HR) coverage and heart rate variability (HRV) parameters were analyzed to evaluate the system. The experimental results showed that the mean of R-peak coverage was 98.0% (95.5%–99.7%), and the normalized errors of HRV time and spectral measures between the Ag/AgCl system and our system ranged from 0.15% to 4.20%. The root mean square errors for inter-beat (RR) intervals and HR were 1.36 ms and 0.09 bpm, respectively. We also showed the potential of our developed system for rapid eye movement (REM) sleep and wake detection as well as for recording of abnormal states.

Correction: Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device. Sensors 2017, 17, 2067

Hyoseon Jeon 1, Woongwoo Lee 2 ID , Hyeyoung Park 2, Hong Ji Lee 1, Sang Kyong Kim 1, Han Byul Kim 1, Beomseok Jeon 2 and Kwang Suk Park 3,* 1 The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea; nulpurunhs@bmsil.snu.ac.kr (H.J.); hongjidan@bmsil.snu.ac.kr (H.J.L.); skkim@bmsil.snu.ac.kr (S.K.K.); hahanbyul@bmsil.snu.ac.kr (H.B.K.) 2 Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea; w2pooh@daum.net (W.L.); 0907bluelove@naver.com (H.P.); brain@snu.ac.kr (B.J.) 3 Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Korea * Correspondence: pks@bmsil.snu.ac.kr; Tel.: +82-2-2072-3135; Fax: +82-2-3676-2821

Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

Investigations of capacitively-coupled EEG electrode for use in brain-computer interface

In this paper, the preliminary investigation for application of capacitive electroencephalogram (EEG) measurement for brain-computer interface (BCI) is described. EEG was obtained by active electrodes that very high input impedance pre-amplifier circuit was mounted on the electrodes. This method enabled EEG measurements through hair without direct contact with scalp and conductive gel applications. The presented electrode was attached to the normal usual baseball cap for nonintrusive daily or long-term use. Subject wore electrode installed baseball cap and EEG signal was measured under experimental BCI paradigms such as alpha wave, steady-state visual evoked potential, P300, auditory steady-state response and motor imagery. The results showed that further studies are still required to apply capacitive sensing methods to clinical neurosciences, however, it can be concluded that steady of the art capacitive electrode technology can be used for engineering applications using current popular BCI paradigms.

Automatic Noise Removal and Peak Detection Algorithm for ECG Measured from Capacitively Coupled Electrodes Included within a Cloth Mattress Pad

Abstract: Recent technological advances have increased interest in personal health monitoring. Electrocardio-gram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascularsystem status. In this study, we developed a capacitive ECG measurement system that can be included within a clothmattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However,it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high levelof noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithmthat can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated bydetermining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Poly-somnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%,and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% forour capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of pro-viding useful ECG information, even under noisy signal conditions.Key words: Capacitively Measured ECG, R-peak Detection, Ubiquitous Healthcare, Non-intrusive ECG MonitoringSystem, MIT-BIH Polysomnographic Database

Development of Online Speller using Non-contact Blink Detection Glasses

Abstract: We proposed blink based online speller for the locked-in syndrome (LIS) patients, paralyzed in nearly allvoluntary muscles expect for the eyes, with a simple and easy-to-use eye blink detection glasses. Electrooculogram(EOG) is the golden standard method of eye movement or blink measurement with Ag/AgCl electrodes. However,this method has several drawbacks such as skin irritation and dehydration of conductive gel. To resolve the short-comings, we used a blink detection system based on a transparent capacitively coupled electrode, which is conductiveindium tin oxide (ITO) films. The films make it possible to measure eye blink without direct skin contact and obstruc-tion of field of view. We finally developed user-friendly blink based online speller with the blink detection system.To classify voluntary and non-voluntary blink, we used the double blink for command of the speller. The online spellerexperiment result with six healthy subjects shows that mean accuracy is 98.96% and letter per minute (LPM) is 4.73,which are better result by comparison with conventional P300 or auditory brain-computer interface (BCI) paradigm.The result of the experiment demonstrates the possibility of applying the proposed system as a communicationmethod for the LIS patients.Key words: Wearable device, ITO film, Blink, Human-Computer Interface, speller

Validation of Algorithm with Noise Tolerance Methods to Detect R-wave

Long-term monitoring of electrocardiogram (ECG) is one of the basic measurement in healthcare and provides decisive information regarding cardiovascular system status. In all ECG applications, the R-wave detection is important. However, it is difficult to detect R-wave automatically because signals obtained in daily life frequently include noise from various sources. Daily life monitoring ECG signal is particularly measured over cloth during walking and sleeping states by using non-invasive sensor, so that it usually presents higher noise level. To improve detection accuracy under noisy condition, we developed algorithm which has some noise tolerance techniques that analyze characteristics of R-wave. The proposed algorithm was evaluated by using the records of the MIT-BIH Polysomnographic database and the data from nonintrusive vital sign monitoring system previously developed in our laboratory. Algorithm reliability was assessed by detection error rate (De), sensitivity (Se) and positive predictivity (P+). The result shows average De of 2.62%, average Se of 98.29% and average P+ of 99.03%. We suggest that our R-wave detection method is useful in the presence of noisy signals.