• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1518-1523
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• 2012

In this study, the novel digital filtering algorithms are implemented to suppress the noisy characteristics embedded in ambulatory electrocardiogram signals by an android smartphone platform. With this aim, Graphical User Interface (GUI) is designed and implemented by utilizing multithread-Java programming to realize Finite Impulse Response and Infinite Impulse Response filter. With simulating our implemented digital filters built in an android smartphone, we can find the fact that we can efficiently suppresses the noisy characteristics due to baseline wandering and 60 Hz powerline source fluctuations especially in electrocardiograms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1143-1149
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• 2010

In this study, we propose a novel method to detect and enhance the feature of ST-segment which offers the crucial information for the diagnosis of myocardial infarction and ischemia. With this aim, PQRST features of Electrocardiogram initially are detected and subsequently ST-segment are estimated. And Dynamic Time Warping(DTW) transformation is applied recursively to minimize the difference in time between ST-segments and calculate the minimum cumulative distance that decides the degree of similarity among ST-segments. As of the results, the inherent characteristic of ST-segment can be emphasized in terms of time parameter and thus the diagnostic features of a ST-segment can be revealed further.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.852-856
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• 2005

This paper proposes a new design of the ECG simulator with high resolution by using small amount of memories based on discrete least square estimation equations instead of reading the stored data inside the look-up table. The experimental results have shown that the ECG simulator using discrete least square estimation equations can display the bipolar limb leads ECG signals with low PRD (percent root-mean-square difference) while taking the less amount of memories than the previous method which used the look-up table to store ECG data for ECG simulation.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.177-180
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• 2002

The type of physiological stress involved in driving is probably complex, and a comprehensive study involving recording of physiological signals such as electrocardiogram(ECG), electromyogram(EMG). Changes in relevant Physiological parameters, such as ECG, EMG, reflected changes in driver status. In order to derive the mental and physical load of driving a motor vehicle from driving behaviour alone it is necessary to establish the relationship between changes in a driver's physiological parameters and behavioral parameters. In this study, we choose two different condition and investigated driver's status using HRV analysis method. Many previous studies have shown that increasing driving time causes a variation of HRV signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.82-87
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• 2004

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.216-222
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• 2013

It is known that blood circulation in human body causes the skin tone to change concurrently with heartbeats. A number of apps have been developed to measure the heartbeat using smartphone camera; however, no any further analysis is performed. In this paper we propose an algorithm that detects heartbeats from the phone's camera and further extracts the heart rate variability (HRV). We compare the HRV extracted from the camera with the HRV extracted from the electrocardiogram. We estimated a number of commonly used HRV characteristics and compared them. Our results show that smartphone camera leads to slightly overestimated characteristics although the difference in extracted HRV signals is negligible. As a consequence we suggest that a smartphone camera can be employed in a quick heart diagnosis and diagnosis of autonomic nervous system.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1918-1919
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• 2007

In this study, dynamic time warping(DTW) is utilized especially for automatically segmenting ECG(Electrocardiogram) signal to extract a periodic time information. For the possible medical application for diagnosing the abnormalities of ECG, the relative metric distance of the warped ECG signals are computed to decide whether the abrupt variations of ECG signal occur or not.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.5-28
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• 2001

This paper investigates a classification method of the electrocardiogram (ECG) into different disease categories. The features for the classification of the ECG are the coefficients of the discrete wavelet transform (DWT) of ECG signals. The coefficients are calculated with Haar wavelet, and after DWT we can get 64 coefficients. Each coefficient has morphological information and they may be good features when conventional time-domain features are not available. Since all of them are not meaningful, it is needed to reduce the size of meaningful coefficients set. The distributions of each coefficient can be the rules to classify ECG signal. The optimally reduced feature set is obtained by fuzzy c-means algorithm and rough set theory. First, the each coefficient is clustered by fuzzy c-means algorithm and the clustered ...

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.239-245
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• 2004

Electrocardiogram and phonocardiogram acquired from ECG and stethoscope by a non-invasive method are bio-signals which laue been a great role in patients diagnosis and used extensively by doctors. And these are mainly used at the examination room or first-and room of hospital. When the patients are located at some places far away from hospital like a car, an airplane and a vessel under way, there are some difficulties in confirming their cardiac sound Especially the patient of ocean going vessel might not have a medical treatment from the professional doctor at times. In this paper, we developed the digital ECG and stethoscope system based on personal computer which can easily measure the patient's cardiac sound and ECG signal, and this might shorten the appearance of the remote maritime telemedicine.