• Journal of Digital Contents Society
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• v.17 no.2
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• pp.97-103
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• 2016

This work introduces techniques experienced for the electrocardiogram(ECG) visual analysis, able to characterize the major parameters and events with clinical relevance for heart failure management and cardiovascular risk assessment. In particular, it includes approaches for ECG data visual processing such as the variable charts, graphs base on the complex MIT-BIH ECG database. Through the experienced this works of ECG database visualization, so many researcher more easily access the complex ECG database and can intuitionally understand the meanings via a variable ECG visualized data.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.1
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• pp.13-18
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• 2010

In this study, heart rate through ECG R-R intervals using the methods about how to automatically extract studied. Heart rate as measured by the naked eye, using the 2-order differential equations to extract heart rate, using self-correlation function to extract the heart rate was compared contemplate. To verify its efficacy and validity in practical applications, these method has been applied to MIT/BIH database. Based on this, making a ECG meter automatic heart rate measurements, and our ECG meter was compared with the existing ICU.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1441-1447
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• 2014

A robust new algorithm for R wave detection named for Multiscale-based Peak Detection(MSPD) is assessed in this paper using MIT/BIH Arrhythmia Database. MSPD algorithm is based on a matrix composed of local maximum and find R peaks using result of standard deviation in the matrix. Furthermore, By reducing needless procedure of proposed algorithm, improve algorithm ability to detect R peak efficiently. And algorithm performance is assessed according to detection rates about various arrhythmia database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1047-1054
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• 2012

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its risk increases with age. Conventionally, the way of detecting AF was the time·frequency domain analysis of RR variability. However, the detection of ECG signal is difficult because of the low amplitude of the P wave and the corruption by the noise. Also, the time·frequency domain analysis of RR variability has disadvantage to get the details of irregular RR interval rhythm. In this study, we describe an atrial fibrillation pattern analysis based on symbolization and information entropy. We transformed RR interval data into symbolic sequence through differential partition, analyzed RR interval pattern, quantified the complexity through Shannon entropy and detected atrial fibrillation. The detection algorithm was tested using the threshold between 10ms and 100ms on two databases, namely the MIT-BIH Atrial Fibrillation Database.

• Journal of the Korea society of information convergence
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• v.6 no.1
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• pp.1-6
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• 2013

The electrocardiogram(ECG) is widely used for the diagnosis of heart disease recent. In order to correct diagnosis, wavelet and thresholding is studied. In this study, we study hard inverse thresholding that is apply the existing hard thresholding. It apply to hard inverse thresholding on Pan-Tomkins algorism, that was simplified. The results of mit-bih No. 103 ECG signal is detected R peaks was detected unaffected by signal distortion and noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.222-226
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• 2008

본 논문에서는 장시간 심전도를 측정하더라고 착용의 불편함을 최소화하기 위해 가슴에 착용 가능한 벨트형 심전도 전극을 제작하였다. 그리고 심전도 신호의 검출을 위하여 초저전력 계측시스템을 구현하였으며, Zigbee호환의 무선노드를 이용하여 계측된 심전도 신호를 PC측으로 무선 전송하는 시스템을 구현하였다. 또한 착용을 통해 장시간 심전도 측정이 가능하도록 움직임에 따른 동잡음을 제거하고자 하였으며, 이를 위해 적응 신호처리기법을 사용하였다. 그리고 가변 기준값을 이용하여 보다 정확한 심전도 R피크를 검출하고자 하였다. 구현된 적응신호처리와 R피크검출의 성능을 평가하기 위하여 MIT-BIH 데이터를 이용한 잡음제거성능 및 피크검출 실험을 수행하였으며, 실제 피검자를 대상으로 활동 중 심전도 계측 실험을 통해 구현된 시스템의 성능평가를 수행하였다.

• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.165-174
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• 2000

본 논문에서는 M 채널 필터 뱅크를 이용하여 심전도 자동 진단 시스템에서 매우 중요한 파라미터로 사용되는 QRS complex 검출을 실시하였다. 제안된 알고리즘에서는 심전도 신호를 M개의 균일한 주파수 대역으로 분할(decomposition)하고, 분할된 서브밴드(subband) 신호들 중에서 QRS complex의 에너지 분포가 가장 많이 존재하는 5∼25Hz 영역의 서브밴드 신호들을 선택하여 feature를 계산함으로써 QRS complex 검출을 실시하였다. 제안된 알고리즘의 성능 비교를 위하여 MIT-BIH arrhythmia database를 사용하였으며, sensitivity는 99.82%, positive predictivity는 99.82, 평균 검출율은 99.67%로 기존의 알고리즘에 비해 높은 검출 성능을 나타내었다. 또한 polyphase representation을 이용하여 M 채널 필터 뱅크를 구현한 결과 연산 시간이 단추되어 실시간 검출이 가능함을 확인하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.471-475
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• 2006

In this paper, we propose a method to eliminate the baseline wander for ECG based on waveform morphology analysis. This method uses the ascending slope tracing waves to approximate the baseline wander in ECG and subtracts these waves from the original ECG to eliminate the baseline wander. This ascending slope tracing waves was developed for efficient enhancement of slope inverting points and sudden slope changing points. This method has been applied to MIT/BIH database to verify its efficacy and validity in practical applications.