Stepwise Detection of the QRS Complex in the ECG Signal

Kim, Jeong-Hong; Lee, Seungmin; Park, Kil−Houm

doi:10.7840/kics.2016.41.2.244

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The type of noise in the ECG signal is a power line interference arising from the device used for acquiring the ECG signal, motion artifacts caused by subjects’ movement, muscle contractions by irregular muscle activity, and baseline drift by breathing [ 23 ]. This noise can provide false information and degrade user recognition performance, so the noise removed process is essential [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

A Study on User Recognition Using the Generated Synthetic Electrocardiogram Signal

Kim

Pan

2021

Sensors

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Electrocardiogram (ECG) signals are time series data that are acquired by time change. A problem with these signals is that comparison data that have the same size as the registration data must be acquired every time. A network model of an auxiliary classifier based generative adversarial neural network that is capable of generating synthetic ECG signals is proposed to resolve the data size inconsistency problem. After constructing comparison data with various combinations of the real and generated synthetic ECG signal cycles, a user recognition experiment was performed by applying them to an ensemble network of parallel structure. Recognition performance of 98.5% was demonstrated when five cycles of real ECG signals were used. Moreover, 98.7% and 97% accuracies were provided when the first cycle of synthetic ECG signals and the fourth cycle of real ECG signals were repetitively used as the last cycle, respectively, in addition to the four cycles of real ECG. When two cycles of synthetic ECG signals were used with three cycles of real ECG signals, 97.2% accuracy was shown. When the last third cycle was repeatedly used with the three cycles of real ECG signals, the accuracy was 96%, which was 1.2% lower than the performance obtained while using the synthetic ECG. Therefore, even if the size of the registration data and that of the comparison data are not consistent, the generated synthetic ECG signals can be applied to a real life environment, because a high recognition performance is demonstrated when they are applied to an ensemble network of parallel structure.

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Section: Resultsmentioning

confidence: 99%

A Study on User Recognition Using the Generated Synthetic Electrocardiogram Signal

Kim

Pan

2021

Sensors

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“…As regards the types of noises that occur while acquiring the ECG signal, there is (i) a power line interference arising from the equipment used for acquiring the ECG signal, (ii) motion artifacts caused by subjects' movement, (iii) muscle contractions by irregular muscle activity, and (iv) baseline drift by breathing [23]. The HPF used in this paper removes signals with frequencies below a certain threshold, and passes those frequencies above it.…”

Section: A Preprocessing For Noise Removal and Ecg Segmentationmentioning

confidence: 99%

Ensemble Networks for User Recognition in Various Situations Based on Electrocardiogram

2020

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Research on electrocardiogram (ECG) signals has been actively undertaken to assess their value as a next generation user recognition technology, because they require no stimulation and are robust against forgery and modification. However, even within the same user, the heart rate and waveform of ECG signals will vary depending on physical activity, mental effects, and measurement time. Therefore, when data acquired across changes in the user state is used as registered data, an overfitting problem occurs due to data generalization, which degrades the recognition performance for newly acquired data. Therefore, in this paper, we propose parallel ensemble networks to solve the overfitting problem and prevent the degradation. First, ECG signals acquired in various environments are used as the input data of parallel convolutional neural networks (CNNs). Each CNN is set up with different parameters to detect different features. The ECG signals outputted from each network are classified for each subject, and then fused into one database to be used as registered data for retraining. Instead of fusing all the output signals from each network, only the ECG signals of Top-3 networks showing excellent performance are fused and composed of registered data. The reconstructed registered data are used for user recognition, by retraining with time independent comparison data in the CNN. The experimental results of comparing the proposed parallel ensemble networks with those of previous studies using the self-acquired actual ECG signals show that the proposed method achieves recognition performance higher than the previous studies, with an accuracy rate of 98.5%. INDEX TERMS ECG, biometrics, user recognition, various situations, parallel ensemble networks.

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Deep Learning-Based Biological Signal Analysis for Assisting Cardiovascular Disease Diagnosis on Mobile Environment

Lee¹,

Kim²,

Kim³

et al. 2017

kics

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Stepwise Detection of the QRS Complex in the ECG Signal

Cited by 4 publications

References 17 publications

A Study on User Recognition Using the Generated Synthetic Electrocardiogram Signal

A Study on User Recognition Using the Generated Synthetic Electrocardiogram Signal

Ensemble Networks for User Recognition in Various Situations Based on Electrocardiogram

Deep Learning-Based Biological Signal Analysis for Assisting Cardiovascular Disease Diagnosis on Mobile Environment

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