• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.104-111
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• 2008

The ECG is biomedical electrical signal occurring on the surface of the body due to the contraction and relaxation of the heart. This signal represents an extremely important measure for health monitoring, as it provides vital information about a patient's cardiac condition and general health. ECG signals are contaminated with high frequency noise such as power line interference, muscle artifact and low frequency nose such as motion artifact. But it is difficult to filter nose from ECG signal, and errors resulting from filtering can distort a ECG signal. The present study implemented a small-size and low-power ECG measurement system that can remove motion artifact for convenient health monitoring during daily life. The implemented ECG monitoring system consists of ECG amplifier, a low power microprocessor, bluetooth module and monitoring program. Amplifier was designed and implemented using low power instrumentation amplifier, and microprocessor was interfaced to the ECG amplifier to collect the data, process, store and feed to a transmitter. And bluetooth module used to wirelessly transmit and receive the vital sign data from the microprocessor to an PC at the receiving site. In order to evaluate the performance of the implemented system, we assessed motion artifact rejection performance in each situation with artificially set condition using adaptive filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.192-200
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• 2015

Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

• Journal of IKEEE
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• v.13 no.1
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• pp.77-86
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• 2009

A wavelet Electrocardiogram(ECG) detector and its analog-to-digital converter(ADC) for low-power implantable cardiac pacemakers are presented in this paper. The proposed wavelet-based ECG detector consists of a wavelet decomposer with wavelet filter banks, a QRS complex detector of hypothesis testing with wavelet-demodulated ECG signals, and a noise detector with zero-crossing points. To achieve high-detection performance with low-power consumption, the multi-scaled product algorithm and soft-threshold algorithm are efficiently exploited. To further reduce the power dissipation, a low-power ADC, which is based on a Successive Approximation Register(SAR) architecture with an on/off-time controlled comparator and passive sample and hold, is also presented. Our algorithmic and architectural level approaches are implemented and fabricated in standard $0.35{\mu}m$ CMOS technology. The testchip shows a good detection accuracy of 99.32% and very low-power consumption of $19.02{\mu}W$ with 3-V supply voltage.

• Science of Emotion and Sensibility
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• v.11 no.3
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• pp.419-426
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• 2008

In order to develop "textile electrode - sensing clothing" which is a sort of smart clothing to measure electric activities of heart, we propose possible ways to develop textile electrode and design of sensing clothing, ultimately aiming to develop "ECG sensing clothing for lifestyle monitoring". Conventional sensors for measuring typical electric activities of heart keep certain distance between measuring electrodes to measure signals for electric activities of heart, but these sensors often cause inappropriate factors (e.g. motional artifacts, inconvenience of use, etc) for monitoring natural cardiac activities in our daily life. In addition, most of textile electrodes have made it difficult to collect data due to high impedance and unstable contact between skin and electrodes. To overcome these questions, we minimized distance between electrodes and skin to maximize convenience of use. And in order to complement contact between skin electrodes, we modified textile electrode's form and developed ways to design clothing. As a result, we could find out clinical significance by investigating possible associations of clinical electrocardiogram (ECG) with variation of distance between electrodes, and could also demonstrate clinically significant associations between textile electrode developed herein and clothing.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.200-207
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• 2014

Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.43-50
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• 2011

Currently, conventional security methods including IC card or password type method are quickly switched into biometric security systems in various applications and the electrocardiogram (ECG) has been considered as one of novel biometrics way. However, conventional ECG based biometrics used lead II signal which conventionally used for formulaic signal to heart disease diagnosis and it is not suitable for biometrics since it is rather difficult to find consistent features for heart disease patents. To overcome this problem, we developed new biometrics system using ECG lead III signals. For wave extraction, signal peak points are extracted through AAV algorithm. For feature selection, extracted waves are categorized into one of four wave types and total twenty two features including number of vertices, wave shapes, amplitude information and interval information are extracted based on their wave types. Experimental results for thirty-six people showed 100% specificity, 95.59% sensitivity and 99.17% of overall identification accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.1-7
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• 2012

In general, currently developed ECG(electrocardiogram) based biometrics approaches are not suitable for real market applications since they require high cost ECG monitoring device and their measurement methods showed poor usability. In this paper, we developed lead I signal based biometrics system using special purpose ECG measurement hardware. To guarantee signal quality for biometrics from various signal measurement environment in our ordinary life, several filters are applied. In addition, to enhance usability, only two skin on electrodes without reference point are used for measurement. Lead I signals of seventeen candidates are measured from developed hardware and features are extracted. Extracted features are applied to support vector machine (SVM) pattern classifier for biometrics, and the experimental results showed 98.59% of sensitivity (SN) and 97.21% of accuracy (ACC). Compare to conventional ECG biometrics approaches, proposed system showed enhanced usability with low-cost measurement hardware.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.418-425
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• 2014

This paper presents a Ku-Band Doppler Radar System to measure respiration and heart rate. It was measured by using simultaneous radar and ECG(Electrocardiogram). Arctangent demodulation without dc offset compensation can be applied to transmitted I/Q(In-phase & Quadrature-phase) signal in order to improve the RMSE(Root Mean Square Error) about 50 %. The power leaked to receiving antenna from the transmitting antenna is always generated because of continuously opening the transceiver of CW(Continuous Wave) Doppler radar. As the output power increase, leakage power has an effect on the SNR(Signal-to-Noise Ratio) of the system. Therefore, in this paper, leakage cancellation technique that adds the signal having the opposite phase of the leakage power to the leakage power was implemented in order to minimize the decline of receiver sensitivity. By applying the leakage cancellation techniques described above, it is possible to measure the heart rate and respiration of the human at a distance of up to 35 m. the heart rate of the measured data at a distance of 35 m accords with the heart rate extracted from the ECG data.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.284-293
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• 2023

Objective: To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). Materials and Methods: This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. Results: For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). Conclusion: The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.

• Journal of IKEEE
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• v.19 no.2
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• pp.237-243
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• 2015

Recently, eight out of ten people have smartphone in Korea. Also, many applications of smartphone have increased. So, smartphone addiction has become a social issue. Especially, many people in smartphone addiction can't control themselves. Sometimes they don't realize that they are smartphone addiction. Many studies, mostly surveys, have been conducted to diagnose smartphone addiction, e.g. S-measure. In this paper, we suggest how to detect smartphone addiction based on ECG and Eye Gaze. We measure the signals of ECG from the Shimmer and the signals of Eye Gaze from the smart eye when the subjects see the emotional video. In addition, we extract features from the S-transform of ECG. Using Eye Gaze signals(pupil diameter, Gaze distance, Eye blinking), we extract 12 features. The classifier is trained using Random Forest. The classifiers detect the smartphone addiction using the ECG and Eye Gaze signals. We compared the detection results with S-measure results that surveyed before test. It showed 87.89% accuracy in ECG and 60.25% accuracy in Eye Gaze.