• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.817-818
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• 2006

In Magnetic Resonance Imaging(MRI), the QRS complex of ECG is used as a trigger signal for MRI scan. But, gradient and RF(radio frequency) artifacts which are caused to static and dynamic field in MRI scanner cause interference in the ECG. Also, the signal shape of theses artifacts can be similar to the QRS-complex, causing possible misinterpretation during patient monitoring and false gating of the MRI. In case of using general FIR or IIR band-pass filters for minimizing the artifacts, artifact-reduction-ratio is not excellent. So, an adaptive real-time digital filter is proposed for reduction of noise by gradient and RF(radio frequency) artifacts. The proposed filter for MRI-Gating is based on the noise-canceller with NLMS(Normalized Least Mean Square) algorithm. The reference signals of the adaptive noise canceller are a combination of the noisy three channel ECG signals. In conclusions, the proposed method showed the acceptable quality of ECG signal with sufficient SNR for gating the MRI and possibility of real time implementation.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1494-1497
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• 2010

휴대형 심전도 단말기의 신호에서 잡음을 제거하고, 파형의 특징점을 찾아 임상파라미터를 추출하는 방법을 제안한다. 실험 결과, 본 방법으로 생성되는 심전도 형태와 임상 파라미터는 전문가의 소견으로 실제 사용상 만족할 만한 수준이었다. 우리의 조사에 의하면, 휴대형 단말기에서와 같이 잡음 수준이 높은 심전도 신호의 잡음 제거 및 해석에 대한 연구는 거의 발표된 적이 없다.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.117-124
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• 1988

A new implementation of a noise tracking filter is prposed to eliminate time-varying 60 Hz noise and its harmonics and baselins wandering in biological signals. This technique was applied to ECG. Filter's notch frequency could track the power line frequency well and it showed much better characteristics than the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1728-1736
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• 2015

Several algorithms have been developed to classify arrhythmia which either rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose arrhythmia classification method using QRS Pattern of ECG signal according to personalized type. For this purpose, we detected R wave through the preprocessing method and define QRS pattern of ECG signal by QRS feature Also, we detect and modify by pattern classification, classified arrhythmia duplicated QRS pattern in realtime. Normal, PVC, PAC, LBBB, RBBB, Paced beat classification is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.98%, 97.22%, 95.14%, 91.47%, 94.85%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1903-1907
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• 2013

Heart condition should be observed for long periods of time because it does not appear abnormal all the time. However, there are many difficulties checking our health for a long time due to its size, operation of equipment, and cost. To solve these problems, an electrocardiograms(ECG), specially interfacing three gel-less metal electrodes for low cost portable applications, is designed and implemented. Gel-less metal electrodes are used for ECG monitoring system instead of gel-type electrodes that can cause skin rashes and itching problem. The whole ECG system consists of two parts-analog and digital circuits. The analog measurement circuit that has a 18*25mm size is made up of op-amps maintaining a sufficiently high common-mode noise rejection and passive elements of SMD type. Analog heart signal is converted to digital stream suitable for display on a TFT-LCD by an 8-bit microcontroller. The size of the completed ECG system is 25*80*50mm and its weighing is about 150g, which is small enough to be easily used. Therefore, the implemented ECG system can be used as a portable one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.551-558
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• 2014

Baseline drift reduction and removal of power line noises in electrocardiogram are often necessary to avoid the distortions in extracting the fiducial features. With this aim, the multirate digital filtering algorithm is suggested to design and implement Finite Impulse Response or Infinite Impulse Response Filter by changing the sampling rate with omitting or interpolating intermediate ECG data. After the experimental simulations performed, we can conclude the fact that we can suppress the baseline wander and power line disturbances in ECG signal with reducing the computational complexities in which we do not keep the original and high sampling frequency.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.1
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• pp.17-22
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• 2011

In this study, a P wave emphasizing and detection algorithm from ECG signal was proposed to read arrhythmia. The algorithm uses two slope tracing waveform, the descending slope tracing wave and the ascending slope tracing wave, developed for efficient determination of slope inverting points and sudden slope changing points. The algorithm generates the slope tracing waveform which trace the original ECG wave, and subtracts one tracing wave from the other to detect P and T waves. The algorithm has been applied to MIT/BIH database in order to verify its efficacy and validity in practical applications.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.459-463
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• 1997

In this paper, we present the elimination of ECG artifact from the polysomnographic EEG and EOG. The idea of this method is that the ECG synchronized EEG segment is detected from ECG and regard samples of that segment a missing signal. After this, we used two interpolation methods to recover the missing segment. One is the Lagrange Polynomial Interpolation Method and the other is the Least Square Error AR Interpolation method. We tested those methods by applying to simulated signals. AR methods works well enough to reject the artifact about 10% of the main artifact level. We practically applied to real EEG and EOG signals. We also developed the algorithm to detect whether the artifact level is high or not. If the artifact level is high, then the interpolations are applied.