• Title/Summary/Keyword: MIT-BIH 데이터베이스

Search Result 66, Processing Time 0.02 seconds

Technique for the ECG Bio-sounds Visualization Analysis Based on the MIT-BIH Database (MIT-BIH 데이터베이스 기반 ECG 생체신호 시각화 분석을 위한 기술)

  • Kim, Jong-Wook;Lee, Myoung-Jin;Ko, Kwang-Man;So, Kyoung-Young
    • Journal of Digital Contents Society
    • /
    • v.17 no.2
    • /
    • pp.97-103
    • /
    • 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.

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification (심전도 신호기반 개인식별을 위한 텐서표현의 다선형 판별분석기법)

  • Lim, Won-Cheol;Kwak, Keun-Chang
    • Smart Media Journal
    • /
    • v.7 no.4
    • /
    • pp.90-98
    • /
    • 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.

A assessment of multiscale-based peak detection algorithm using MIT/BIH Arrhythmia Database (MIT/BIH 부정맥 데이터베이스를 이용한 다중스케일 기반 피크검출 알고리즘의 검증)

  • Park, Hee-Jung;Lee, Young-Jae;Lee, Jae-Ho;Lim, Min-Gyu;Kim, Kyung-Nam;Kang, Seung-Jin;Lee, Jeong-Whan
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.63 no.10
    • /
    • pp.1441-1447
    • /
    • 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.

Patient Adaptive Pattern Matching Method for Premature Ventricular Contraction(PVC) Classification (조기심실수축(PVC) 분류를 위한 환자 적응형 패턴 매칭 기법)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.16 no.9
    • /
    • pp.2021-2030
    • /
    • 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.

Atrial Fibrillation Pattern Analysis based on Symbolization and Information Entropy (부호화와 정보 엔트로피에 기반한 심방세동 (Atrial Fibrillation: AF) 패턴 분석)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.16 no.5
    • /
    • pp.1047-1054
    • /
    • 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.

Atrial Fibrillation Detection Algorithm through Non-Linear Analysis of Irregular RR Interval Rhythm (불규칙 RR 간격 리듬의 비선형적 특성 분석을 통한 심방세동 검출 알고리즘)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.15 no.12
    • /
    • pp.2655-2663
    • /
    • 2011
  • Several algorithms have been developed to detect AF which rely either on the form of P waves or the based on the time frequency domain analysis of RR variability. However, locating the P wave fiducial point is very difficult because of the low amplitude of the P wave and the corruption by 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 detection algorithm through non-linear analysis of irregular RR interval rhythm based on the variability, randomness and complexity. We employ a new statistical techniques root mean squares of successive differences(RMSSD), turning points ratio(TPR) and sample entropy(SpEn). The detection algorithm was tested using the optimal threshold on two databases, namely the MIT-BIH Atrial Fibrillation Database and the Arrhythmia Database. We have achieved a high sensitivity(Se:94.5%), specificity(Sp:96.2%) and Se(89.8%), Sp(89.62%) respectively.

Optimal Threshold Setting Method for R Wave Detection According to The Sampling Frequency of ECG Signals (심전도신호 샘플링 주파수에 따른 R파 검출 최적 문턱치 설정)

  • Cho, Ik-sung;Kwon, Hyeog-soong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.21 no.7
    • /
    • pp.1420-1428
    • /
    • 2017
  • It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.

Comparison of PVC Detecting Methods with ECG Using Descending Slope Tracing Waves and Form Factor (하강 기울기 추적파와 Form Factor를 이용한 심전도 조기심실수축의 검출 방법의 비교)

  • Ju, Jangkyu;Lee, Ki Young
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
    • /
    • v.1 no.3
    • /
    • pp.21-26
    • /
    • 2008
  • In this paper, we extracted descending slope tracing waves (DSTW) and form factors (FF), and compared the detecting results of premature ventricular contraction (PVC) which were derived from DSTW and FF in order to find an efficient method. The 2nd. derivatives and DSTW were employed to extract correct R-waves from ECG. To evaluate extracting methods, ECGs including PVCs from MIT/BIH database were used.

  • PDF

Arrhythmia Classification using Hybrid Combination Model of CNN-LSTM (합성곱-장단기 기억 신경망의 하이브리드 결합 모델을 이용한 부정맥 분류)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.26 no.1
    • /
    • pp.76-84
    • /
    • 2022
  • Arrhythmia is a condition in which the heart beats abnormally or irregularly, early detection is very important because it can cause dangerous situations such as fainting or sudden cardiac death. However, performance degradation occurs due to personalized differences in ECG signals. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-LSTM. For this purpose, the R wave is detected from noise removed signal and a single bit segment was extracted. It consisted of eight convolutional layers to extract the features of the arrhythmia in detail, used them as the input of the LSTM. The weights were learned through deep learning and the model was evaluated by the verification data. The performance was compared in terms of the accuracy, precision, recall, F1 score through MIT-BIH arrhythmia database. The achieved scores indicate 92.3%, 90.98%, 92.20%, 90.72% in terms of the accuracy, precision, recall, F1 score, respectively.

Arrhythmia Classification using GAN-based Over-Sampling Method and Combination Model of CNN-BLSTM (GAN 오버샘플링 기법과 CNN-BLSTM 결합 모델을 이용한 부정맥 분류)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.26 no.10
    • /
    • pp.1490-1499
    • /
    • 2022
  • Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.