• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.38-46
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• 2012

Atrial fibrillation is needed to be detected at paroxysmal stage and to be treated. But, paroxysmal atrial fibrillation ECG is hardly obtained with 12-lead electrocardiographs but Holter systems. Presently, the averaged beat subtraction(ABS) method is solely used to estimate atrial fibrillatory waves even with somewhat large residual error. As an alternative, in this study, we suggested an ESAF(event-synchronous adaptive filter) based algorithm, in which the AF ECG was treated as a primary input and event-synchronous impulse train(ESIT) as a reference. And, ESIT was generated so to be synchronized with the ventricular activity by detecting QRS complex. We tested proposed algorithm with simulated AF ECGs and real AF ECGs. As results, even with low computational cost, this ESAF based algorithm showed better performance than the ABS method and comparable performance to algorithm based on PCA(principal component analysis) or SVD(singular value decomposition). We also proposed an expanded version of ESAF for some AF ECGs with multi-morphologic ventricular activities and this also showed reasonable performance. Ultimately, with Holter systems including our proposed algorithm, atrial activity signal can be precisely estimated in real-time so that it will be possible to calculate atrial fibrillatory rate and to evaluate the effect of anti-arrhythmic drugs.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.75-80
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• 1999

Since a Holter monitor records a patients' ECG when he is in motion, it can monitor complex heart disease effectively. But it can not handle a sudden heart disease because the diagnosis process will be done only after 24 hours. So in this study, a model of ambulatory monitoring system using wireless data communication network is proposed. And a mobile ECG equipment and a doctors' terminal are developed for the proposed system implementation. As a result, we can evaluate that the proposed ambulatory monitoring system is suitable for the management of ambulatory patients who may be at risk form sudden cardiac abnormalities.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.251-260
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• 1998

The Holter monitoring system is a widely used noninvasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we design a high performance intelligent holter monitoring system which is characterized by the small-sized and the low-power consumption. The system hardware consists of one-chip microcontroller(68HC11E9), ECG preprocessing circuit, and flash memory card. ECG preprocessing circuit is made of ECG preamplifier with gain of 250, 500 and 1000, the bandpass filter with bandwidth of 0.05-100Hz, the auto-balancing circuit and the saturation-calibrating circuit to eliminate baseline wandering, ECG signal sampled at 240 samples/sec is converted to the digital signal. We use a linear recursive filter and preprocessing algorithm to detect the ECG parameters which are QRS complex, and Q-R-T points, ST-level, HR, QT interval. The long-term acquired ECG signals and diagnostic parameters are compressed by the MFan(Modified Fan) and the delta modulation method. To easily interface with the PC based analyzer program which is operated in DOS and Windows, the compressed data, that are compatible to FFS(flash file system) format, are stored at the flash memory card with SBF(symmetric block format).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.512-515
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• 2001

The heifer electrocardiograph is a widely progressing research which is used in judgement to existence for a heart disease and analyzes electric potential changes of heart movement during continuous living of everyday after attaching to body portable holler recorder for twenty-four hours. In order to obtain electrocardiogram clearly, it must eliminate that 60Hz power line interference, baseline wandering, noise of muscle constriction. In holter electrocardiogram, the most big problem which is recorded signal of ECG(electrocardiogram) is a baseline wandering elimination, which is occurred by rhythm of respiration and muscle constriction of part from attaching to an electrode. Such baseline is roughly irregular wandering and shaking up and down therefore the part of baseline wandering elimination is very important because it is difficult of ECG diagnosis. In this study, it is stabilized baseline wandering in analog part as implementation f real-time signal processing digital filter so it is applicable to analyze patient's heart disease by way of design of baseline wandering elimination system.

• 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.

• Journal of Yeungnam Medical Science
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• v.24 no.2
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• pp.216-231
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• 2007

Background : An acceleration or deceleration of the heart rate (HR), which reflects autonomic effects, is observed before the onset of paroxysmal atrial fibrillation (PAF). The purpose of this study was to assess the discrepancy in the autonomic interactions before the onset of PAF for different patterns of change in the HR. Materials and Methods : From 105 Holter tapes with the PAF recorded, 55 episodes (42 patients, 34 men, $58{\pm}12$ years) of PAF (>5 min), preceded by a sinus rhythm for more than 1 hour, were selected and submitted to time-domain and frequency-domain HR variability analyses. Fifty-five episodes were divided into 2 groups: group A PAF (n=30) with acceleration of the HR during the last 2 minutes before the PAF and group B (n=25) with deceleration of the HR. Results : A significant linear decrease in the mean R-R interval was observed in group A ($924{\pm}30$ to $835{\pm}28ms$, $P$=0.001) and an increase from $831{\pm}32$ to $866{\pm}31ms$ in group B PAF episodes ($P$=0.046). In the frequency-domain analyses, the LF/HF ratio exhibited a progressive linear increase before the PAF in group A ($P$=0.005). The HF normalized units (HFnu) and natural logarithm-transformed HF ($In$HF) values decreased from $30.8{\pm}4.0$ to $16.1{\pm}1.8$ ($P$=0.003) and $4.49{\pm}0.25$ to $4.07{\pm}0.22$ ($P$=0.001), respectively. Contrary to the results in group A, a significant increase in the HF components (HFnu and $In$HF) (from $22.6{\pm}3.2$ to $30.2{\pm}4.0$, $P$=0.005, and $4.27{\pm}0.27$ to 4.75 0.33, $P$=0.001, respectively) and a resultant decrease in the LF/HF ratio were observed in group B PAF episodes. No significant changes were observed in the LF components in either PAF group. Conclusion : Autonomic stimuli leading to an acceleration or deceleration of the HR before the onset of AF are due to parasympathetic modulation. Parasympathetic modulation plays a key role in the initiation of PAF.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.17-23
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• 2011

In this paper, daily life stress monitoring system is proposed. The proposed wireless ECG module, reducing the noise and increasing the size of signal, amplification circuit was designed for. Using HRV(Heart Rate Variability), extracted by measuring R-wave, stress diagnostic algorithms to assess the stress of human emotion were developed. For monitoring the activities, the proposed system is consist of small rectangular size for portable and by simple measurement it is possible to measure at any time. Through experiments, the proposed approach to represent user's stress level can be confirmed. Through that, it can see appropriate structure to obtain R-wave for stress assess as well as high resemblance to the clinical electrocardiogram. In this paper, performed experiments was developed nonrestraint measuring and wearable wireless biometric scanner that is able to monitor the heart's electrical activity of everyday life.Using this, the algorithm system, that is able to assess stress index through time-domain and frequency-domain analysis of the front and the rear of performing stress load protocol, was developed,

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.96-104
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• 2006

In this study, data transmission ratio and power consumption issues of Zigbee based sensor module and personal digital assistant(PDA) were addressed to develop ECG telemetry device. PDA processes the data transmitted through serial port using non-blocking method. The transmission rate was dependent on the packet structure. It was 300 ECG samples/sec, when each packet was composed of 2 ECG data and 3-axial acceleration vector. Using two AAA batteries in series, operating time of the wireless sensor module was above 28 hours in average. Power consumption of PDA was dependent on screen ON/OFF condition and serial port usage. In this application, operating time of PDA was 5 hours in average. In conclusion, there was no problem in the power consumption of wireless sensor module and transmission rate, when the developed device was used as 24 hour Holter device. But, PDA has the problem of power consumption, which should be solved.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.464-469
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• 2015

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.