• Journal of Biomedical Engineering Research
• /
• v.25 no.6
• /
• pp.589-598
• /
• 2004

We propose a new algorithm to extract the fetal electrocardiogram (FECG) from a multi-channel electrocardiogram (ECG) recorded at the chest and abdomen of a pregnant woman. To extract the FECG from the composite abdominal ECG, the classical time-domain method based on singular value decomposition (SVD) has been generally used. However, this method has some disadvantages, such as its high degree of computational complexity and the necessary assumption that vectors between the FECG and the maternal electrocardiogram (MECG) should be orthogonal. The proposed algorithm, which uses SVD in a discrete cosine transform (DCT) domain, compensates for these disadvantages. To perform SVD with lower computational complexity, DCT coefficients corresponding to high-frequency components were eliminated on the basis of the properties of the DCT coefficients and the frequency characteristics of the FECG. Moreover, to extract the pure FECG with little influence of the direction of the vectors between the FECG and MECG, three new channels were made out of the MECG suppressed in the composite abdominal ECG, and the new channels were appended to the original multi-channel ECG. The performance of the proposed algorithm and the classical time-domain method based on SVD were compared using simulated and real data. It was experimentally verified that the proposed algorithm can extract the pure FECG with reduced computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.2 no.1
• /
• pp.11-17
• /
• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, p, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detecting techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source. In this paper, we performed the extracting parameters from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Journal of Biomedical Engineering Research
• /
• v.35 no.6
• /
• pp.211-218
• /
• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.10
• /
• pp.733-740
• /
• 2004

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) is proposed. The whole system consists of two-lead electrocardiogram acquisition (lead Ⅰ and Ⅱ), baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problem of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we proposed a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 subjects, and we could obtain satisfactory respiration signals that shows high correlation (r>0.9) with the signal acquired from the chest-belt respiration sensor.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.8
• /
• pp.39-46
• /
• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, P, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detection techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source. In this paper, we performed the extracting parameters from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2361-2376
• /
• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• Journal of Magnetics
• /
• v.17 no.2
• /
• pp.133-137
• /
• 2012

We investigated the effects of pulsed magnetic fields (PMF) stimulus on electroencephalogram (EEG) alpha activity and heart rate variability (HRV) from electrocardiogram (ECG) measurements with various stimulus durations at acupoint PC9. The alpha activity in the EEG and the ratio of low frequency power and high frequency power (LHR) in the HRV, a reflection of sympathovagal activity, were increased and decreased, respectively, after PMF stimulus of 3 min. Our spectral analysis quantitatively proved that the changes in the EEG alpha activity were consistent with an autonomic function in the ECG. These findings suggest that appropriate PMF stimulus results in the same effect as that of acupuncture applied to the acupoint PC9, which is closely related to the parasympathetic activity of the autonomic nervous system.

• Journal of Biomedical Engineering Research
• /
• v.6 no.2
• /
• pp.53-62
• /
• 1985

The position and time interval of wave components of the electrocardiogram are used as important data for physician's diagnosis. In case of using the existing definition of the onset of the wave component of the electrocardiogram, they have some problems of defining the precise position of the isoelectric line, of defining the limit of the gradient accepted as the onset, and of the gradient being changed by noise. Therefore, in this paper all time intervals and positions of wave components needed for data of diagnosis were obtained correctly by turning point data reduction algorithm and linear regression intersection algorithm, and the viability of the method of intersecting lines was established in comparison to the four methods of calculating the PR interval.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2010.07a
• /
• pp.35-38
• /
• 2010

본 논문에서는 일상생활 속에서 무자각적으로 생체신호를 측정하고 분석하여 감성을 평가할 수 있는 임베디드 시스템에 관하여 연구하였다. 지속적으로 변화하는 감성을 일관적이며 신뢰성이 높은 생리적인 방법으로 평가하기 위해 심전도(ECG:Electrocardiogram), 맥파(PPG:Photoplethysmogram)의 두 가지 생체신호를 측정하고, 무선전송(Bluetooth) 장치를 이용하여 측정한 생체신호를 실시간으로 노트북PC로 전송하여 분석하였다. 생체신호의 분석방법은 FFT(Fast Fourier Transform)과 전력스펙트럼밀도(Power Spectrum Density)를 이용한 주파수 분석방법으로 두 생체신호의 특정 주파수 대역이 가지는 자율신경계의 활성도의 비율을 분석하여 비교 연구하였다. 또한 보다 빠르고 정확한 감성을 평가하기 위하여 분석알고리즘의 연산을 최소화 하였으며 그래프를 이용한 분석결과의 시각화를 하였다. 본 논문에서는 무자각적인 생체신호 측정 시스템을 이용하여 다양한 상황에서 생체신호를 측정하고, 개발한 분석 알고리즘으로 분석한 결과의 차이를 연구하여 정확도 및 신뢰도를 기준으로 감성을 평가하기 위한 분석 시스템을 평가하였다.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.20 no.6
• /
• pp.81-87
• /
• 1983

The body surface ECG(electrocardiogram) is produced by the electric fields caused by the propagation of action potentials within the myocardial cells. The repolarization phase of the action potential is very sensitive to factors of clinical importance. Therefore, in this paper of the inverse electrocardiography, we studied a method of estimating the uniform action potentials during repolarization phase from the body surface ECG using digital signal identification techniques. The estimated action potential of a normal was similar to that of clinical data in the repolarization phase.