Yong Gyu Lim

Flexible Capacitive Electrodes for Minimizing Motion Artifacts in Ambulatory Electrocardiograms

This study proposes the use of flexible capacitive electrodes for reducing motion artifacts in a wearable electrocardiogram (ECG) device. The capacitive electrodes have conductive foam on their surface, a shield, an optimal input bias resistor, and guarding feedback. The electrodes are integrated in a chest belt, and the acquired signals are transmitted wirelessly for ambulatory heart rate monitoring. We experimentally validated the electrode performance with subjects standing and walking on a treadmill at speeds of up to 7 km/h. The results confirmed the highly accurate heart rate detection capacity of the developed system and its feasibility for daily-life ECG monitoring.

The effect of missing RR-interval data on heart rate variability analysis in the frequency domain

In this study, optimal methods for re-sampling and spectral estimation in frequency-domain heart rate variability (HRV) analysis were investigated through a simulation using artificial RR-interval data. Nearest-neighbour, linear, cubic spline and piecewise cubic Hermite interpolation methods were considered for re-sampling and representative non-parametric, parametric, and uneven approaches were used for spectral estimation. Based on this result, the effects of missing RR-interval data on frequency-domain HRV analysis were observed through the simulation of missing data using real RR-interval tachograms. For this simulation, data including the simulated artefact section (0-100 s) were used; these data were selected randomly from the real RR data obtained from the MIT-BIH normal sinus rhythm RR-interval database. In all, 7182 tachograms of 5 min durations were used for this analysis. The analysis for certain missing data durations is performed by 100 Monte Carlo runs. TF, VLF, LF and HF were estimated as the frequency-domain parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters were calculated. Rules obtained from the results of these simulations were evaluated with real missing RR-interval data derived from a capacitive-coupled ECG during sleep.

Automatic ballistocardiogram (BCG) beat detection using a template matching approach

This paper suggests a beat detection method for ballistocardiogram (BCG) from an unconstrained cardiac signal monitoring devices. A fiducial peak point of BCG is an I-J-K complex which corresponds with ventricle contraction and Electrocardiogram (ECG) QRS complex. The goal of the method is extraction of J peak without ECG synchronization. The detection method is based on a “template matching” rule evaluated using a correlation function in a local moving-window procedure. The total beat detection algorithm operates in two stages, template definition stage and beat detection stage with defined template in previous stage. In the first stage, the BCG template is constructed by the expert with an empirical analysis of BCG signal and measurement device. In the second stage, the correlation function calculates an accuracy of template with BCG signal using a local moving-window. The data analysis has been performed on the subjects tested at Seoul National University Hospital Sleep Medicine Center and presents 95.16% of sensitivity and 94.76% of positive predictivity value for the J peak detection.

Effect of missing RR-interval data on heart rate variability analysis in the time domain

In this study, the effects of missing RR-interval data on time-domain analysis were investigated using simulated missing data in real RR-interval tachograms and actual missing RR data in an ECG obtained by an unconstrained measurement. For the simulation, randomly selected data (0-100 s) were removed from real RR data obtained from the MIT-BIH normal sinus rhythm database. In all, 2615 tachograms of 5 min durations were used for this analysis. For certain durations of missing data, the analysis was performed by 1000 Monte Carlo runs. MeanNN, SDNN, SDSD, RMSSD and pNN50 were calculated as the time-domain parameters in each run, and the relative errors between the original and the incomplete tachograms for these parameters were computed. The results of the simulation revealed that MeanNN is the parameter most robust to missing data; this feature can be explained by the theory of finite population correction (FPC). pNN50 is the parameter most sensitive to missing data. MeanNN was also found to be the most robust to real missing RR data derived from a capacitive-coupled ECG recorded during sleep; furthermore, the parameter patterns for the missing data were considerably similar to those for the original RR data, although the relative errors may exceed those of the simulation results.

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.

Brain–computer interfaces using capacitive measurement of visual or auditory steady-state responses

OBJECTIVE Brain-computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. APPROACH A capacitive electrode that does not require an electrolytic gel or direct electrode-scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. MAIN RESULTS Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. SIGNIFICANCE The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Effect of missing RR-interval data on nonlinear heart rate variability analysis

The effects of missing RR-interval data on nonlinear heart rate variability (HRV) analysis were investigated using simulated missing data in actual RR-interval tachograms and actual missing RR-interval data. For the simulation study, randomly selected data (ranging from 0 to 100s) were removed from actual data in the MIT-BIH normal sinus rhythm RR-interval database. The selected data are considered as a simulated artefact section. In all, 7182 tachograms of 5-min duration were used for this analysis. For each missing interval, the analysis was performed by 100 Monte Carlo runs. Poincaré plot, detrended fluctuation, and entropy analysis were executed for the nonlinear HRV parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters, were calculated. In this process, the usefulness of reconstruction was considered, for which bootstrapping and several interpolation methods (nearest neighbour, linear, cubic spline, and piecewise cubic Hermite) were used. The rules for the reconstruction, derived from the results of these simulations, were evaluated with actual missing RR-interval data obtained from a capacitive-coupled ECG during sleep. In conclusion, nonlinear parameters, excepting Poincaré-plot-analysis parameters, may not be appropriate for the accurate HRV analysis with missing data, since these parameters have relatively larger error values than time- or frequency-domain HRV parameters. However, the analysis of the long-term variation for nonlinear HRV values can be available through applying the rules for the reconstruction obtained in this study.

REM Sleep Classification with Respiration Rates

Polysomnagraphy, the method evaluating the sleep quality, is the reliable and standard method. However, to perform it, subject has to attach many sensors to their body. It seems intrusive, tangled and interrupting the subjects sleep. For the sake of monitoring the daily sleep pattern, simple method measuring the biosignal nonintrusively is needed. At that point, respiration is a beneficial signal, because it can be easily acquired by check the change of the thorax or abdomen. Respiration is one of the representatives of the reflection of the autonomic nervous system. According to the transition of the sleep stages, autonomic nervous system also changes and it can be shown by respiration. In this study, REM and non-REM sleep is classified by only using respiration for the 3 subjects. To enhance the performance, activity signal acquired by load-cell installed bed is added to divide the wake and REM sleep. At the results, hypnograms with polysomnagraphic data is compared with them. It can be seen that high correlation between the REM sleep and increasing pattern of the respiration rates.

Capacitive driven-right-leg grounding in Indirect-contact ECG measurement

For the reduction of common-mode noise level in Indirect-contact ECG (IDC-ECG) measurement, a driven-right-leg grounding method was applied to the IDC-ECG. Because the IDC-ECG does not require any direct contact between the electrodes and the human skin, it is adequate for un-constraining long-term ECG measurement at home and its various applications are now under development. However, larger 60 Hz noise induced by power line appears in IDC-ECG than in conventional ECG, that is a restriction of IDC-ECG application. In this study, the driven-right-leg ground which has been used in conventional direct-contact ECG, was adapted to the IDC-ECG measurement, by feedback of the inversion of amplified common-mode noise to the body through the conductive textile laid on the chair seat. It was shown that the level of 60Hz power line noise was reduced to about -40 dB when the driven-right-leg gain was 1000.