• 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 Semiconductor & Display Technology
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• v.11 no.3
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• pp.57-61
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• 2012

In order to analyze the ECG, it is important to pinpoint the repeat interval of the ECG. In this paper, we present an efficient algorithm for finding the QRS complex on the ECG. We escaped from the conventional methods to go through multiple steps of preprocessing to make ECG easier to find the QRS complex. We selected the candidate peak that has the possibility of the QRS complex in original ECG, and used technique to find the QRS complex among the candidate peak. In this way, we could get similar efficiency to Pan & Tompkins Algorithm that is most representative QRS complex detection algorithm, just used fewer operations than Pan & Tompkins Algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.403-409
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• 2012

This paper proposes an energy efficient ECG monitoring system by putting some intelligence on the sensor node to reduce the number of transmissions. The sensor node is mostly put into the processing mode and just connects the base station when necessary. Therefore, the transmission energy is greatly reduced while the energy for processing is increased a little bit. Our proposed ECG analysis method classifies ECG cycles by computing the Euclidean distance between the sensed ECG cycle and the reference ECG cycle. This work is a detailed and full explanation of our former work. Extended experimental results show that the proposed trade is very effective in saving energy and the Euclidean distance based classification method is accurate. Furthermore, the PowerTOSSIM energy simulation method is also demonstrated as very accurate in evaluating the energy consumption of the sensor node in our application scenario.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.1-6
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• 2019

As mobile technologies such as Internet of Things (IoT)-based smart homes and financial technologies (FinTech) are developed, authentication by smart devices is used everywhere. As a result, presence-based biometric authentication using smart devices has become a new mainstream in knowledge-based authentication methods like the existing passwords. The electrocardiogram (ECG) is less prone to forgery, and high-level personal identification is its unique feature from among various biometric authentication methods, such as the pulse, fingerprints, the face, and the iris. Biometric authentication using an ECG is receiving a great deal of attention due to its uses in healthcare and FinTech. In this study, we implemented an ECG authentication system that allows users to easily measure and authenticate their ECG waveforms using a miniaturized wearable device, rather than a large and expensive measurement device. The implemented ECG authentication system identifies ECG features through P-Q-R-S-T feature point identification, and was user-certified under the proposed authentication protocols. Finally, assessment of measurements in a majority of adult males showed a relatively low false acceptance rate of 1.73%, and a low false rejection rate of 4.14%, in a stable normal state. In a high-activity state, the false acceptance rate was 13.72%, and the false rejection rate was 21.68%. In a high-heart rate state, the false acceptance rate was 10.48%, and the false rejection rate was 11.21%.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.11
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• pp.1174-1185
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• 2012

This study develops tight-fitting upper clothing to measure electrocardiography (ECG) data. Taking into consideration the elasticity of the clothing, we made 4 experimental clothes by applying to each a weft reduction rate of 40%, 50%, 60%, and 70%. The 4 experimental clothes were used to measure resting ECG, exercise ECG, and post-exercise ECG for 4 men in their 20s. We compared clothing pressures using sensors on the human body and on a dressform. Subjective wear sensations of the 4 experimental clothes were evaluated using a subjective 7-point scale (with 7 being most excellent). We measured clothing pressures by using the air type pressure (AMI 3037-2) for upper and lower chest sensors in the developed tight-fitting upper clothing. The lower chest sensor showed that the clothing pressure on a human body and dressform changed consistently as the weft reduction rate decreased. The upper chest sensor showed inconsistent changes in clothing pressure as the weft reduction rate decreased. The wearing-test result for preliminary subjects showed that the lower chest sensor was more stable than the upper chest sensor; therefore, we inserted the sensor at the lower chest position before performing ECG. Except for Subject 4, the resting ECGs were stably measured for 3 subjects (Subject 1, Subject 2, and Subject 3) in all the developed clothes (A clothing, B clothing, C clothing, and D clothing). However, D clothing showed stable ECG values after exercise. The results of the experiment showed that we could measure ECG without difficulty using clothes with a weft reduction rate of 40% when the movement was not intense; however, tight-fitting upper clothing with a weft reduction rate of 70% was necessary to measure exercise ECG and post-exercise ECG values.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.421-425
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• 2006

An ubiquitous healthcare system for the home care of elderly persons was designed and implemented using wireless sensor network technology. The wireless technology for home-care purpose gives new possibilities for monitoring of vital parameter with wearable biomedical sensors, and will give the patient the freedom to be mobile and still be under continuously monitoring and thereby to better quality of patient care. Emphasis is placed on recent advances in wireless ECG system for cardiac event monitoring with particular attention to arrhythmia detection in patient. This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using wireless sensor technology. The system also provides an application for recording activities, events and potentially important medical symptoms. The hardware allows data to be transmitted wirelessly from on-body sensor to the base station and then to PC/PDA. Data is also transmitted to a back-end server for analysis using wireless internet connection. Experiments were conducted using the system for activity monitoring, exercise monitoring and medical screening tests and present preliminary data and results.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.98-103
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• 2007

Long term Electrocardiogram (ECG) [1] analysis plays a key role in heart disease analysis. A combined detection of QRS-complex and P-wave in ECG signal for ubiquitous healthcare system was designed and implemented which can be used as an advanced warning device. The ECG features are used to detect life-threating arrhythmias, with an emphasis on the software for analyzing QRS complex and P-wave in wireless ECG signals at server after receiving data from base station. Based on abnormal ECG activity, the server will transfer alarm conditions to a doctor's Personal Digital Assistant (PDA). Doctor can diagnose the patients who have survived from cardiac arrhythmia diseases.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.271-278
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• 1995

An ECG data compression algorithM using max-min slope update is proposed and a real time 3 channel ECG transmission system is implemented using the proposed algorithm. In order to effectively compress ECG data, we compare a threshold value with the max-min slope difference (MMSD) which is updated at each sample values. If this MMSD value is smaller than the threshold value, then the data is compressed. Conversely, when the MMSD value is larger than threshold value, the data is transmitted after storing the value and the length between the data which is beyond previous threshold level. As a result, it can accurately compress both the region of QRS, P, and T wave that has fast-changing and the region of the base line that slope is changing slow. Therefore, it Is possible to enhance the compression rate and the percent roms difference. In addition, because of the simplicity, this algorithm is more suitable for real-time implementation.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.269-273
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• 2001

Proper determination of pacing threshold is important for patient safety and pacemaker longevity. In general, cardiac muscle contractions caused by pacing pulses are verified by observing the morphology of surface ECG displayed on a monitor. In this study, a method of automatic pacing threshold determination based on morphological difference between intrinsic and paced ECGs was developed. First, characteristics of intrinsic ECG and paced ECG were analyzed in time and frequency domain and a proper discrimination parameter was extracted. Then, the automatic capture verification method based on the parameter was developed and applied to 23 pacemaker patients. The selected parameter was the area of ventricular depolarization wave during 80ms after pacing stimulus. It was found that the method was reliable and effective in identifying paced ECG and, thereby, determing a proper pacing threshold.

• Korean Journal of Digital Imaging in Medicine
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• v.12 no.1
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• pp.1-4
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• 2010

The study was designed to transmit ECG(electrocardiogram) images from iPhone in order to check validity. Especially, web examined ECG transmissions from smart phones image viewer are good enough to secure clinical information and confirmed with visual. In result, it took 3 to 5 seconds for ECG viewer transmitting from a smart phone to display an image on mobile server to user's phone. For image valuation, we magnified the ECG image 100% or 200% at a resolution of $480{\times}320$ checking PQRST wave and found that it was both possible to see an accurate PQRST wave with all the trial data and to divide it into 1mm level of grid.