Jeong Su Lee

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.

Validation of heart rate extraction through an iPhone accelerometer

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.

REM sleep estimation only using respiratory dynamics

Polysomnography (PSG) is currently considered the gold standard for assessing sleep quality. However, the numerous sensors that must be attached to the subject can disturb sleep and limit monitoring to within hospitals and sleep clinics. If data could be obtained without such constraints, sleep monitoring would be more convenient and could be extended to ordinary homes. During rapid-eye-movement (REM) sleep, respiration rate and variability are known to be greater than in other sleep stages. Hence, we calculated the average rate and variability of respiration in an epoch (30 s) by applying appropriate smoothing algorithms. Increased and irregular respiratory patterns during REM sleep were extracted using adaptive and linear thresholds. When both parameters simultaneously showed higher values than the thresholds, the epochs were assumed to belong to REM sleep. Thermocouples and piezoelectric-type belts were used to acquire respiratory signals. Thirteen healthy adults and nine obstructive sleep apnea (OSA) patients participated in this study. Kappa statistics showed a substantial agreement (kappa > 0.60) between the standard and respiration-based methods. One-way ANOVA analysis showed no significant difference between the techniques for total REM sleep. This approach can also be applied to the non-intrusive measurement of respiration signals, making it possible to automatically detect REM sleep without disturbing the subject.

Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI

BACKGROUND Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) generate weak SSVEP with a monitor and cannot use harmonic frequencies, whereas P300-based BCIs need multiple stimulation sequences. These issues can decrease the information transfer rate (ITR). NEW METHOD In this paper, we introduce a novel hybrid SSVEP-P300 speller that generates dual-frequency SSVEP, allowing it to overcome the abovementioned limitations and improve the performance. The hybrid speller consists of nine panels flickering at different frequencies. Each panel contains four different characters that appear in a random sequence. The flickering panel and the periodically updating character evoke the dual-frequency SSVEP, while the oddball stimulus of the target character evokes the P300. A canonical correlation analysis (CCA) and a step-wise linear discriminant analysis (SWLDA) classified SSVEP and P300, respectively. Ten subjects participated in offline and online experiments, in which accuracy and ITR were compared with those of conventional SSVEP and P300 spellers. RESULTS The offline analysis revealed not only the P300 potential but also SSVEP with peaks at sub-harmonic frequencies, demonstrating that the proposed speller elicited dual-frequency SSVEP. This dual-frequency stimulation improved SSVEP recognition, increased the number of targets by employing harmonic frequencies, reduced the stimulation time for P300, and consequently improved ITR as compared to the conventional spellers. COMPARISON WITH EXISTING METHODS The new method reduces the stimulation time and allows harmonic frequencies to be employed for different stimuli. CONCLUSIONS The results indicate that this study provides a promising approach to make the BCI speller more reliable and efficient.

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.

A new approach of unconstrained sleep monitoring and pulse arrival time extraction using PPG pillow and CC-ECG electrode system

In this paper, an unconstrained and noninvasive system to detect heart rate (HR), respiratory rate and extract pulse arrival time (PAT) during sleep is proposed. The system consists of PPG sensor module embedded in a pillow and CC-ECG (capacitor coupled-ECG) sensor module. From the PPG sensor, we acquired heart rate and respiratory rate during sleep with simple peak detection algorithm. Besides, by employing extraction algorithms, PAT can be extracted from signal acquired with a CC-ECG electrode placed on a mattress. The result shows that HR and respiratory rate detection and PAT extraction have been successfully completed regardless of sleep position. This study suggests that the proposed system is promising to be used for continuous and unconstrained estimation of blood pressure during sleep.

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.