• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.825-832
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• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

• Journal of Yeungnam Medical Science
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• v.6 no.1
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• pp.99-107
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• 1989

24-hour ambulatory ECG monitoring has been examined for the evaluation of heart rate and longest pause in 34 patients with chronic atrial fibrillation(20 patients treated with digoxin and 14 patients without treatment). Following results were obtained : 1. In 34 patients, the mean of average heart rates was $75.7{\pm}13.8$/minute, fastest heart rates $148.0{\pm}32.4$/minute, slowest heart rates $48.1{\pm}8.4$/minute, difference between fastest and slowest heart rates in individual patients $99.9{\pm}29.0$/minute and longest pauses $2.95{\pm}1.06$seconds. The longest pauses of more than 4.0 seconds occurred in 4 of the 34 patients and made an exeption of comparison groups. 2. In 27 of the 34 patients, ventricular premature contractures were developed and in 11 of 27, mainly occured less than 100/24 hours and aberrant conduction occurred in all patients. 3. In 20 patients treated with digoxin(0.25mg/day), the mean of average heart rates was $78.4{\pm}13.7$/minute, fastest heart rates $152.5{\pm}33.1$/minute, slowest heart rates $48.9{\pm}8.5$/minute, difference between fastest and slowest heart rates in individual patients $103.6{\pm}31.7$/minute and longest pauses $2.55{\pm}0.50$seconds. 4. In 10 patients without treatment, the mean of average heart rates was $78.0{\pm}10.7$/minute, fastest heart rates $154.5{\pm}26.8$/minute, slowest heart rates $50.6{\pm}7.1$/minute, difference between fastest and slowest heart rates in individual patients $103.9{\pm}22.2$/minute and longest pauses $2.66{\pm}0.39$seconds. 5. The difference of heart rates and longest pauses between patients with treatment and without treatment were statistically not significant(P>0.05). In summary, authors seemed to consider that 24-hour ambulatory ECG was useful and safe method for clinical evaluation of patients with chronic atrial fibrillation.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.57-64
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• 1990

This study describes the design of the portable intelligent QT analyzer which can record and analyze the ambulatory ECG data. System hardware is consisted of the one chip microcomputer(80C31) , A/D, ROM, RAM, LCD display and preamplifier. ECG data were processed by the differentiator and the digital filter. The de- tection of the parameters-QT, QTP and RR interval-was accomplished by the software algorithm using the slope and the amplitude of the processed data. Using this system, the trends of the parameters obtained during the long term could be observed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1109-1115
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• 2008

In the ubiquitous health monitoring environments, it is quite important not only to evaluate the physiological health condition but also mental stress condition. In order to achieve this goal, a heart activity monitoring system utilizing a wearable bipolar electrode is devised and the heart rate variability(HRV) is extracted and interpreted in both frequency and time feature domains. Consequently, to evaluate the emotional stress condition of the subjects, a stress-induced experimental protocol was applied to healthy subjects and the time and frequency features of heart activity were analyzed in terms of the ratio of low frequency components v.s., high frequency components and the relevant the moving average distributions compromising the successive RR peaks intervals in the ambulatory ECG measurement system.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.351-360
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• 1989

This paper describes the design of portable arrhythmia monitor and associated algorithm for automated diagnosis based-on microcomputer in the ambulatory ECG recording, analysis, and transmitting to a hospital host computer immediately through the telephone system. The device differs from Molter recorder in that it does not store normal ECG signals but captures and alarms the ECG during suspected abnormal periods and selected temporal epochs to a central hospital site. This porta file arrhythmia monitor makes use of a general purpose computer and software will be changed to meet the custom requirements of individual physicians and patients. At present it is very obvious that each cardiologist has his own method of analyzing ECG recordings and utilizes past experience more than the firm quantitative analysis of data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1331-1338
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• 2007

A new method for detecting and eliminating the Electrocardiogram(ECG) artifact from the scalp Electroencephalogram(EEG) is proposed. Based on the single channel EEG, the proposed method consists of 4 procedures: emphasizing the R-wave of ECG artifact from EEG using the least squares acceleration(LSA) filter, detecting the R-wave from the LSA filtered EEG using the phase space method and R-R interval, generating the delayed impulse synchronized to the R-wave and elimination of the ECG artifacts based on the adaptive digital filter using the impulse and raw EEG. The performance of the proposed method was evaluated in the two separating parts of R-wave detection and, ECG estimation and elimination from EEG. In the R-wave detection, the proposed method showed the mean error rate of 6.285(%). In the ECG estimation and elimination using simulated and/or real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, in which independent component analysis and ensemble average method are used. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifact from single channel EEG and simple for ambulatory/portable EEG monitoring system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.834-837
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• 2005

In this paper, we describe the method of non-invasive blood pressure measurement using pulse wave transit time(PWTT). PWTT is a new parameter involved with a vascular that can indicate the change of BP. PWTT is measured by continuous monitoring of ECG and pulse wave. No additional sensors or modules are required. In many cases, the change of PWTT correlates with the change of BP. We measure pulse wave using the photo plethysmograph(PPG) sensor in an earlobe and we measure ECG using the ECG monitoring device our made in the chest. The measurement device for detecting pulse wave consists of infrared LED for transmitted light illumination, pin photodiode as light detector, amplifier and filter. We composed 0.5Hz high pass, 60Hz notch and 10Hz low pass filter. ECG measurement device consists of multiplexer, amplifier, filter, micro-controller and RF module. After amplification and filtering, ECG signal and pulse wave is fed through micro-controller. We performed the initial work towards the development of ambulatory BP monitoring system using PWTT. An earlobe is suitable place to measure PPG signal without the restraint in daily work. From the results, we can know that the dependence of PWTT on BP is almost linear and it is possible to monitoring an individual BP continuously after the individual calibration.

• 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 KIEE Conference
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• 2007.07a
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.