• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.109-112
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• 1995

This paper is a study on the detection of the significant point in ECG signal. ECG signal consists of two components; one is high frequency component to be detected and the other is low frequency component to be removed. AR model is appropriate for modelling and removing the low frequency component. AR model coefficients are updated by artificial neural network algorithm. We can remove the background noise(low frequency) by passing through the AR filter. The remaining signals which include high frequency noise are sent to the matched filter to pass only the signal which we want to extract. The template used in matched filter is updated adaptively.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.61-70
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• 2023

We investigated technological trends in an electrocardiogram (ECG)-based biometric cryptosystem that uses physiological features of ECG signals to provide personally identifiable cryptographic key generation and authentication services. The following technical details of the cryptosystem were investigated and analyzed: preprocessing of ECG signals, extraction of personally identifiable features, generation of quantified encryption keys from ECG signals, reproduction of ECG encryption keys under time-varying noise, and new security applications based on ECG signals. The cryptosystem can be used as a security technology to protect users from hacking, information leakage, and malfunctioning attacks in wearable/implantable medical devices, wireless body area networks, and mobile healthcare services.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.299-305
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• 1999

In thls paper, the representative beal textraction algorIthms for the diagnostic parameter extraction in noisy signal were compared. We used the avernge, median, mode, and trmmed mean to calculale the central tendency. In our experimenl, we have restricted to four kinds of noises -EMG noise, 60Hz powerline inlerference, ahrupl baseline shift, and baselme drift due to respimtion-which were commonly occurred in ECG mgnal, then we have calculated signal-to-noise ratios(SNRs) for the ECG corrupted with each noise and all noises together. As the result of this paper, we have proved that the average method has super lor performance than the others in the ECG corrupted wilh EMG noise. When the signal mcludes extreme value such as abrupt baseline shIft, the median, mode, trimmed mean methods have supenor performance in the SNR ratios. Especially when the ECG corrupted with baseline drift due to respirallon, the trimmed mean method was most efficient because ST level change was 0 V.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.201-206
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• 2012

The indirect-contact ECG measurement is a newly developed method for unconstrained and nonconscious measurement in daily life. This study introduced a new method of electrode circuit design developed for reducing the 60Hz power line noise observed at the indirect-contact ECG measurement. By the introduced common-mode-driven shielding, the voltage of the electrical shield surrounding the capacitive coupling electrode is maintained at the same as the common mode voltage. Though the method cannot reduce the level of common mode voltage itself, that reduces effectively the differential mode noise converted from the common mode voltage by the difference of cloth impedance between the two capacitive coupling electrode. The experiment results using the actual indirect-contact ECG showed that the 60Hz power line noise was reduced remarkably though the reduction ratio was smaller than the expected by the theory. Especially, the reduction ratio became large for the large difference of cloth. It is expected that the introduced method is useful for reducing the power line noise under condition of poor electrical grounding.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• Science of Emotion and Sensibility
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• v.11 no.2
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• pp.235-244
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• 2008

This study is to develop automatic extraction system of continuous blood pressure using ECG (Electrocardiogram) and PPG(Photoplethysmography) for u-health care technology. PTT (Pulse Transit Time) was determined from peak difference between ECG and PPG and its inverse made to get blood pressure. Since the peaks were vulnerable to be contaminated from noise and variation of amplitude, this study developed the adaptive algorithm for peak calculation in any noise condition. The developed method of the adaptive peak calculation was proven to make the standard deviations of PPT decrease to 28% and the detection of noise increase to 18%. Also, the correlation model such as blood pressure = -0.044 $\cdot$ PTT + 133.592 has successfully been determined for predicting the continuous pressure measured without using cuff but with using PPG and ECG, only.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.6
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• pp.366-371
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• 2017

In this paper, we introduce an electrocardiogram (ECG) system designed to solve problems caused by wetgels and motion artifacts in measuring active movement. The system is called a dry-contact ECG and was designed by considering impedance matching between skin and electrode as well as the frictional electricity between electrode and clothes. In order to create the system, we measured impedance on the skin-electrode interface, and the result was applied to the electronic circuit scheme. Moreover, we added an electrode on the back of the measurement electrode to make a flow path to ground the electrical noise. The final ECG circuit and novel electrode were used to detect real human cardiac signals from a subject who was tested while standing still and walking. The signals obtained from the two activities were nicely shaped, without any motion artifact noise. We took electrode size into account in this study because the impedance depended on the area of the electrode. An electrode of 50 mm diameter showed the best curve for the ECG signal without any electrical noise.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.343-350
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• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

• 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.