• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.2
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• pp.191-199
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• 2008

In this paper, we present a performance analysis and design and implementation results of a 2.4 GHz bio-radar system that can detect human heartbeat and respiration signals. In order to design a 2.4 GHz bio-radar system qualitatively, we investigate the electromagnetic properties of human tissues and calculate the target SNR of demodulation output with respect to distance. The target SNR is defined by the 90 % success ratio for detecting heartbeat signal. With this target SNR value, the performance and link budget of the bio-radar system is simulated using MATLAB. Using this link budget results, the direct conversion receiver is designed and Implemented in 4 layer printed circuit board(PCB). With output power of 0 dBm and 5 Hz bandwidth, 80 % success ratio of 50 cm is measured. Measurement results show a good agreement with simulation results.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.543-549
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• 2013

Motion artifacts of central and autonomic nervous system signals degrades the performance of the bio-signal based human factor analysis. Firstly, we propose a defining method of motion artifact section by analyzing successive image frames. Motion artifact section is defined when the amount of motion is greater than the pre-defined threshold. In here, the amount of motion is estimated by first derivation of image frames at temporal domain. Secondly, we propose another defining method of motion artifact section through designing 2D Gaussian probability density function model by analyzing feature vectors of one cycle of signal such as length and amplitude. The defined motion artifact sections are interpolated on the basis of 1D Gaussian function. At result of applying the method into photoplethysmography signal, we confirmed that the calculated heartbeat rate from the restored photoplethysmography came up to the one from electrocardiography. Also, we found that the video based method generated relatively more false acceptance of motion artifact section and the probability density function based method generated relatively more false rejection of motion artifact section.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.131-139
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• 2013

In this paper, we look into previous research in relation to each processing step for ECG diagnosis and propose detection and classification method of arrhythmia using rhythm features of ECG signal. Rhythm features for distribution of rhythm and heartbeat such as identity, regularity, etc. are extracted in feature extraction, and rhythm type is classified using rule-base constructed in advance for features of rhythm section in rhythm classification. Experimental results for all of rhythm types in the MIT-BIH arrhythmia database show detection performance of 100% for arrhythmia with only normal rhythm rule and applicability of classification for rhythm types with arrhythmia rhythm rules.

• Smart Media Journal
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• v.7 no.4
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• pp.90-98
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• 2018

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification Electrocardiogram signals, included in the cardiac electrical activity, are often analyzed and used for various purposes such as heart rate measurement, heartbeat rhythm test, heart abnormality diagnosis, emotion recognition and biometrics. The objective of this paper is to perform individual identification operation based on Multilinear Linear Discriminant Analysis (MLDA) with the tensor feature. The MLDA can solve dimensional aspects of classification problems in high-dimensional tensor, and correlated subspaces can be used to distinguish between different classes. In order to evaluate the performance, we used MPhysionet's MIT-BIH database. The experimental results on this database showed that the individual identification by MLDA outperformed that by PCA and LDA.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.17-23
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• 2020

As the number of households with companion animals increases, the demand to monitor the health of companion animals in remote locations far away is increasing. We are going to put a wearable device on a companion animal so that it can monitor the heart beat signal from a remote location. However, the monitoring method using Bluetooth has some disadvantages. it can be accessed only in a short distances. In case of WiFi, large power consumption is the problem. To overcome these issues, we propose a system to reduce power consumption by indirectly receiving a user's request using Bluetooth at a time when the user does not need it, and sending sensor data through WiFi when the user makes a monitoring request.

• Journal of electromagnetic engineering and science
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• v.10 no.4
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• pp.250-255
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• 2010

This paper presents a non-contact measurement method of vital signal by the use of multiple-input multiple-output (MIMO) bio-radar system, configured with two antennas that are separated by a certain distance. The direction of arrival (DOA) estimation algorithm for coherent sources was applied to detect vital signals coming from different spatial angles. The proposed MIMO bio-radar system was composed of two identical transceivers sharing single VCO with a PLL. In order to verify the performance of the system, the DOA estimation experiment was completed with respect to the human target at angles varying between $-50^{\circ}$ and $50^{\circ}$ where the bio-radar system was placed at distances (corresponding to 50 cm and 95 cm) in front of a human target. The proposed MIMO bio-radar system can successfully find the direction of a human target.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.342-343
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• 2007

In this paper, we developed an integrated miniaturized device which acquires and transmits the signal of ECG an interested heartbeat and body's temperature. Electrocardiogram(ECG) is a recording of the electrical activity on the body surface generated by heart. ECG & temperature measurement is collected by wireless sensor (for Ag/AgCl Thin-Film) placed at designated locations on the body. It is that dual wireless sensor will apply variously to Ubiquitous & Healthcare System.

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.3
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• pp.173-183
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• 2015

In obstetrics, cardiotocography is a procedure to record the fetal heartbeat and the uterine contractions usually during the last trimester of pregnancy. It helps to monitor patterns associated with the fetal activity and to detect the pathologies. In this paper, random forest classifier is used to classify normal, suspicious and pathological patterns based on the features extracted from the cardiotocograms. The results showed that random forest classifier can detect these classes successfully with overall classification accuracy of 93.6%. Moreover, important features are identified to reduce the feature space. It is found that using seven important features, similar classification accuracy can be achieved by random forest classifier (93.3%).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.2
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• pp.208-217
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• 2010

This paper presents a novel 10 GHz bio-radar system based on a frequency multiplier and phase-locked loop(PLL) for non-contact measurement of heartbeat and respiration rates. In this paper, a 2.5 GHz voltage controlled oscillator (VCO) with PLL is employed to as a frequency synthesizer, and 10 GHz continuous wave(CW) signal is generated by using frequency multiplier from 2.5 GHz signal. This paper also presents the noise characteristic of the proposed system. As a result, a better performance and economical frequency synthesizer can be achieved with the proposed bio-radar system. The experimental results shows excellent bio-signal measurement up to 100 cm without any additional digital signal processing(DSP), and the proposed system is validated.