• Title/Summary/Keyword: 부정맥 분류

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A Search for Analogous Patients by Abstracting the Results of Arrhythmia Classification (부정맥 분류 결과의 축약에 기반한 유사환자 검색기)

  • Park, Juyoung;Kang, Kyungtae
    • KIISE Transactions on Computing Practices
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    • v.21 no.7
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    • pp.464-469
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    • 2015
  • Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.

Prediction of arrhythmia using multivariate time series data (다변량 시계열 자료를 이용한 부정맥 예측)

  • Lee, Minhai;Noh, Hohsuk
    • The Korean Journal of Applied Statistics
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    • v.32 no.5
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    • pp.671-681
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    • 2019
  • Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

Efficient QRS Detection and PVC(Premature Ventricular Contraction) Classification based on Profiling Method (효율적인 QRS 검출과 프로파일링 기법을 통한 심실조기수축(PVC) 분류)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.3
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    • pp.705-711
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    • 2013
  • QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

Extracting Arrhythmia Classification Fuzzy Rules Using A Neural Network And Wavelet Transform (퍼지 신경망과 웨이블릿 변환을 이용한 부정맥 분류 퍼지규칙의 추출)

  • Kim Deok-Yong;Lim JoonShik
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.11a
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    • pp.110-113
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    • 2005
  • 본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted fuzzy Membership Funcstions, NEWFM)을 이용하여 심전도 신호로부터 조기심실수축(Premature Ventricular Contraction, PVC)을 판별하는 퍼지규칙을 추출하고 있다. NEWFM은 자기적응적(self adaptive) 가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이를 기반으로 정상 파형과 PVC 파형을 구분한다. 분류 성능 평가를 위하여 MIT/BIH 부정맥 데이터 베이스를 사용하였으며, NEWFM의 입력은 심전도의 파형에 웨이블릿 변환을 적용하여 추출된 웨이블릿 계수를 사용하였다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 계수를 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화 시킨다. 이러한 방법으로 추출된 2개의 웨이블릿 계수를 사용한 퍼지규칙은 $96\%$의 PVC 분류성능을 보여준다.

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Classification of Premature Atrial Contraction using Feature of ECG Signal based on Error Back-Propagation (오류 역전파 기반 ECG 특징을 이용한 심방조기수축(PAC) 분류)

  • Jeon, EunKwang;Nam, Yunyoung;Lee, Hwa-Min
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.04a
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    • pp.669-672
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    • 2017
  • 최근 한국인의 주요 사망원인 중 하나로 부정맥이 부각되고 있다. 심방조기수축(PAC:Premature Atrial Contraction)은 심방이 동방결절의 명령이 있기 전에 수축해 버리는 것이다. 심방조기수축은 일시적으로 유발하였다 사라지곤 할 수 있기 때문에 심한 증상이 없다면 생명에 위협을 가하진 않지만 반대의 경우에는 위험할 수 있다. 따라서 비정상적인 심장 박동이 발생하면 이를 검출하여 조기에 부정맥을 진단할 수 있는 방법이 필요하다. 이를 위해 대상의 ECG 신호로부터 QRS패턴에 해당하는 특징들을 추출하였고 특징들을 이용하여 심방조기수축 파형을 분류한다. 오류 역전파 기반으로 특징들을 훈련하며 가중치와 바이어스값을 구한뒤 이를 이용하여 정상파형과 심방조기수축 파형을 분류한다.

Arrhythmia Surgery in Fontan Operation (폰탄 수술에서의 부정맥수술)

  • 임홍국;한국남;김웅한;이정렬;노준량;김용진
    • Journal of Chest Surgery
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    • v.37 no.8
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    • pp.644-651
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    • 2004
  • Background: Refractory atrial arrhythmias in patients late after the Fontan operation result in significant morbidity and mortality. We reviewed our experience with arrhythmia surgery in patients who had Fontan operation. Material and Method: Between July 1986 and December 2003, 275 early survivors after Fontan operation were reviewed. Fourteen patients underwent. arrhythmia surgery at reoperation after Fontan operation, and mean age at reoperation was 16.8$\pm$7.1 (range: 4.5 ∼ 30.6) years. Mechanisms of arrhythmia included atrial flutter in 8 patients, and atrial fibrillation in 2. Arrhythmia surgery has evolved from isthmus cryoablation in 12 patients to right-sided maze in 2 patients. Thirty-two patients. underwent prophylactic isthmus cryoablation concomitantly at initial Fontan operation. Result: Postoperative arrhythmias occurred in 68 patients (24.7%) among 275. There was no early and late mortality after the arrhythmia surgery. After redo Fontan operation, all patients maintained normal sinus rhythm. Atrial flutter recurred in 3 patients who had sinus conversion with medication and 7 required permanent pacemakers with a mean follow-up of 26.5$\pm$29.1 (range: 2 ∼ 73) months. All patients have improved to NYHA class I or II. After prophylactic cryoablation at initial Fontan operation, 29 patients (90.6%) had sinus rhythm, 1 patient had junctional tachycardia, 1 patient had sinus nodal dysfunction, and 1 patient had AV block with a mean follow-up of 51.3$\pm$19.8 (range: 4∼80) months. Conclusion: Redo Fontan operation, and concomitant arrhythmia surgery reduced atrial arrhythmias and improved NYHA functional classification.

Optimal Parameter Extraction based on Deep Learning for Premature Ventricular Contraction Detection (심실 조기 수축 비트 검출을 위한 딥러닝 기반의 최적 파라미터 검출)

  • Cho, Ik-sung;Kwon, Hyeog-soong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.12
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    • pp.1542-1550
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    • 2019
  • Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. 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 97.84% in PVC classification.

Design of Fuzzy System for Decision of Arrhythmia using Wavelet Coefficients (웨이브렛 계수를 이용한 부정맥 판정용 퍼지시스템 설계)

  • Kim, Min-Soo;Seo, Hee-Don
    • Journal of Sensor Science and Technology
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    • v.11 no.4
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    • pp.230-238
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    • 2002
  • In this paper, we designed a fuzzy system using the wavelet coefficients to detection the PVCs effectively and to increase the accuracy of decision of the arrhythmia. In the proposed Fuzzy system, the QRS complex of ECG signal is divided into 6th level frequence bands by wavelet transform using Haar wavelet. The MIT/BIH database for the source of input signal is used in order to evaluate the performance of the proposed system. From the simulation results, the decision of membership functions for PVCs and heart rates by using Fuzzy rules, we detected the abnormal values effectively by application of leaned from neural network and we also found results in classification ratio of 95% the decision of arrhythmia.

Adaptive Detection of Unusual Heartbeat According to R-wave Distortion on ECG Signal (심전도 신호에서 R파 왜곡에 따른 적응적 특이심박 검출)

  • Lee, SeungMin;Ryu, ChunHa;Park, Kil-Houm
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.9
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    • pp.200-207
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    • 2014
  • Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.