• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.337-340
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• 2009

Wireless tele-home-care application gives new possibilities for ECG (electrocardiogram) monitoring system with wearable biomedical sensors. Thus, continuously development of high convenient ECG monitoring system for high-risk cardiac patients is essential. This paper describes to monitor a person's ECG using wearable approach. A wearable belt-type ECG electrode with integrated electronics has been developed and has proven long-term robustness and monitoring of all electrical components. The measured ECG signal is transmitted via an ultra low power consumption wireless sensor node. ECG signals carry a lot clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed thus it bring errors due to motion artifacts and signal size changes. Variable threshold method is used to detect the R-peak which is more accurate and efficient. In order to evaluate the performance analysis, R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research. This concept able to allow patient to follow up critical patients from their home and early detecting rarely occurrences of cardiac arrhythmia.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1444-1452
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• 1989

This paper describes the implementation of a real time fetal ECG monitoring system in which an adaptive multi-channel noise canceller is realized using the Texas Instruments TMS32020 progrmmmable ditital signal processor. An ECG signal from the electrode placed on the mother's abdomen and three ECGs from those on the chest are applied as the desired signal and the referened inputs, respectively, of the multi-channel filter. The coefficients of the filter are updated using the LMS algorithm such that the output of the multi-channel filter copies the maternal ECG embedded in the abdominal ECG. The enhanced fetal ECG is obtained by subtracting the filter output from the abdominal ECG, and the difference signal is recorded. Both off-line and on-line experimental results are presented to verify the effectiveness of the parameters for the digital signal processing algorithms and the prototype system.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.31-35
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• 1996

This paper presents a real time algorithm for monitoring of the arrythmia of ECG signal. A real time monitoring, following by detecting a QRS complex, is the most important. Using 2-dimensional time-delay coordinates which are reconstructed by the phase portrait plotting special trajectory, we detect QRS complexes. In this study, arrythmias are detected by matching the past standard template with tile present pattern when changing abruptly In order to matching with each other, we propose modified chain coding algorithm which applies vetor table consisting of eight orthonormal code(=binary code) to the phase portraits. This algorithm using logical function increases the weight if exceeding to the threshold determinded by correlation value and the distance from a straight line(y=x). Evaluating the performance of the proposed algorithm, we use standard MIT/BIH database. The results are fellowing, 1) Improve the speed of matching template than that of cross-correlation ever has been used. 2) Because the proposed algorithm is robust to varing fiducial point, it is possible to monitor the ECG signal with irregular RR interval. 3) In spite of baseline wandering owing to the low frequency noise, monitoring performance is not reduced.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.702-712
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• 2016

A wrist watch type wearable cardiovascular monitoring device is proposed for continuous and convenient monitoring of the patient's cardiovascular system. For comprehensive monitoring of the patient's cardiovascular system, the concurrent electrocardiogram (ECG) and arterial pulse wave (APW) sensor front-end are fabricated in $0.18{\mu}m$ CMOS technology. The ECG sensor frontend achieves 84.6-dB CMRR and $2.3-{\mu}Vrms$-input referred noise with $30-{\mu}W$ power consumption. The APW sensor front-end achieves $3.2-V/{\Omega}$ sensitivity with accurate bio-impedance measurement lesser than 1% error, consuming only $984-{\mu}W$. The ECG and APW sensor front-end is combined with power management unit, micro controller unit (MCU), display and Bluetooth transceiver so that concurrently measured ECG and APW can be transmitted into smartphone, showing patient's cardiovascular state in real time. In order to verify operation of the cardiovascular monitoring system, cardiovascular indicator is extracted from the healthy volunteer. As a result, 5.74 m/second-pulse wave velocity (PWV), 79.1 beats/minute-heart rate (HR) and positive slope of b-d peak-accelerated arterial pulse wave (AAPW) are achieved, showing the volunteer's healthy cardiovascular state.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2163-2164
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• 2006

It is desirable to monitor the vital signals, such as ECG, of a emergency patient during transfer in the hospital as well as in the ambulance. The purpose of this study is to develop a system which provides a real-time and wireless ECG to the medical staff nearby patient during transfer in hospital. In this context, we developed a low-power, low-cost and portable ECG system consisting of 1) ECG measurement and RF transmission module and 2) RF receiving and LCD display module. The developed system is expected to be useful in monitoring ECG of a patient during transfer in the hospital.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.27-32
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• 2002

In this study, we fabricated the advanced telemetry system that transmitting media use radio frequency(RF) for the middle range measurement of the physiological signals and receiving media use optical for electromagnetic interference problem. The telemetry system within a size of 65$\times$125$\times$45mm consists of three parts: RF transmitter, optical receiver and physiological signal processing CMOS one chip. Advantages of proposed telemetry system is wireless middle range(50m) FM transmission, reduce electromagnetic interference to a minimum which enables a comfortable bed-side telemetry system. The monitoring system was designed in the structure of dual-processor for the real time processing. The use of the one channel in our study made it possible the real time wavelet transformation of electrocardiogram data of 360Hz, 16 bits for every 1.42 seconds.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.41-43
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• 2005

PDA-based monitoring is used to acquire continuously the patient's vital signs, including electrocardiography, activity, heart rate and $SpO_2$. In this paper, A biomedical signal acquisition device was designed using 3-axial MEMS accelerometer and 1-ch ECG amplifier, to have the function of monitoring activity and electrocardiography. The proposed system is composed of transmitter and receiver. Through the Zigbee communication, subject's biosignals can be transmitted in real-time to receiver, and transmitted data confirmed using PDA. The packet size used in this device was set not to exceed a maximum payload size of 116 byte. One packet consists of two segments. The transmission speed was 21 packet per second, 420 ECG samples per second, and 42 acceleration samples per second. The proposed method can be used to develop Activities of Daily Living(ADL} monitoring devices for the elderly or movement impaired people and enables patients to be monitored without any constraints. Also, this method will reduce medical costs in the aged society.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.45-50
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• 2021

With the recent development of IT technology, research and interest in various bio-signal measuring devices are increasing. But studies related to ECG(electrocardiogram), which is one of the most representative bio-signals, particularly arrhythmic signal detection, are incomplete. Since arrhythmia has various causes and has a poor prognosis after onset, preventive treatment through early diagnosis is best. However, the 24-hour Holter electrocardiogram, a tool for diagnosing arrhythmia, has disadvantages in the limitation of use time, difficulty in analyzing motion artifact due to daily life, and the user's real-time alarm function in danger. In this study, an ECG and pulse monitoring device capable of continuous measurement for a long time, a real-time monitoring app, and software for analysis were developed, and the trend of the measured values was confirmed. In future studies, research on derivation of quantitative results of ECG signal measurement analysis is required, and further research on the development of an arrhythmic signal detection algorithm based on this is required.