• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.728-735
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• 2013

In this paper, we present a method of transmitting ECG signals in real-time mobile environment to be possible to implement the ubiquitous healthcare system. Because of the excessive amount of data transmission of ECG signals, it is necessary to propose a limitation to the real-time transmission. We propose a real-time electrocardiographic monitoring system based on the proposal of unusual waveform detection algorithm which detects the R-wave distortions from the arrhythmia ECG signals having unusual waveform of about 10% on average. It is very effective in terms of time and cost for medical staffs to monitor and analyze ECG signals for a long period of time. Monitoring unusual waveform by gradually adjusting the threshold values of potential and kurtosis makes the amount of data transmitted decrease and significance level of waveform to be enhanced. The unusual waveform detection algorithm is implemented with ubiquitous environment inter-working device client. It is applicable to ubiquitous healthcare system capable of real-time monitoring the ECG signal. While ensuring the mobility, it allows for real-time continuous monitoring of ECG signals.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.453-456
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• 2005

일반적으로 심전도는 심장계통의 질환을 판단할 때 사용된다. 이러한 심장질환의 이상 유무를 자동으로 진단하기 위해서는 QRS파형 검출을 필요로 하며, 이를 위하여 웨이블렛변환 방법이나 템플릿매칭, 룰 베이스 방법 등 여러 가지 방법들이 쓰이고 있으나, 심전도 신호가 표준화된 형태를 갖지 않는 경우는 검출 능력에 많은 한계를 갖고 있다. 본 논문은 파형의 베이스라인(baseline)을 기준으로 진폭 값에 절대치을 취하는 방법으로 파형의 R피크값을 검출하는 알고리즘을 제안한다. 결과를 검증하기 위해 MIT-BIH 데이타베이스에서 제공하는 데이터와 R피크값을 본 논문의 알고리즘으로 추출된 R피크값과 비교한 결과 96.7%의 검출률을 보였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.145-151
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• 2012

The pulsimeter is a representative device in the oriental medicine, which can analysis a risk factor in a cardiovascular diseases. However, most of the previous methods have the limit by the contacted sate of the brachial pulse and sensor in the measuring time, the inaccuracy of detected pulse, and the difficulty of pulse analysis. Accordingly, we propose the moving pulse image analysis based pulsimeter that can acquire a pulse of patient in real time by analyzing a moving image. then this video is shot the state change of the T.S. occurred by a pulse in capillary. In order to evaluate the performance of the our pulsimeter, we measured a respective detecting-rate about the essential 5 feature-points in the pulse analysis from the detected original pulse. As a result, the proposed method is very successful.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.727-732
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• 2012

This paper presents an approach to classify normal and arrhythmia from the MIT-BIH Arrhythmia Database using Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT) and neural network. In the first step, Discrete Cosine Transform is used to obtain the representative 15 coefficients for input features of neural network. In the second step, Discrete Wavelet Transform are used to extract maximum value, minimum value, mean value, variance, and standard deviation of detail coefficients. Neural network classifies normal and arrhythmia beats using 55 numbers of input features, and then the accuracy rate is 98.8%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.15-21
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• 2009

This paper proposes a novel algorithm to assess the abnormal heart beats such as PVC (Premature Ventricular Contraction) and its subsequent RUNs. Our Arrhythmic detection scheme is based on only the R-R Interval features extracted from ECG waveforms and MIT-BIH arrhythmia database is evaluated to validate the efficiency of our algorithm in terms of sensitivity, specificity, FPR(%) and FNR(%).

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.205-206
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• 2008

한의학에서 손목의 맥파를 손으로 짚어 보고 파형의 변화로써 병변의 원인을 파악하는 것은 기본적인 진단 방법이다. 진단을 위해 요골동맥의 촌, 관, 척 세 부위의 맥파를 수지를 통해 일정 가압을 하며 맥의 깊이, 강약, 빠르기 등을 촉진을 통해 맥상을 분석한다. 본 연구에서는 촌, 관, 척 부위를 동시 측정하기 위해 각 부위에 5개의 압력센서 어레이가 적용된 15채널 맥진기를 구성하였다. 또한 요골 동맥 부위에 적정 가압을 유지하기 위해 공기 커프를 이용한 공압 가압 방식을 적용하였다. 본 가압 방식을 통해 요골 동맥 부위에 연속적이며 정밀 가압제어가 가능하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.355-356
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• 2013

심장의 활동을 전기적 변위로 표현되는 심전도 신호는 심장병 진단에 중요한 임상적 파라미터들을 제공한다. 특히 심전도 신호에서 P, QRS Complex,, T 특징점들로 대표되는 파형 변곡점들의 시간상 위치와 크기 및 형태학적 모양은 심장의 이상 리듬을 나타내는 부정맥여부를 검출하는데 핵심적인 역할을 한다. 본 연구에서는 특히 QRS complex 구간에 대한 첨도치의 연산 해석을 통하여 정상적인 심전도 리듬과 심실조기수축 부정맥 리듬을 구분하는 방법을 제시하고 또한 스마트폰을 기반으로 하는 심전도 모니터링 시스템에 적용하고자 하였다.

• Proceedings of the Korean Magnestics Society Conference
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• 2012.11a
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• pp.137-139
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• 2012

• 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 Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.