Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Feature Extraction based on Auto Regressive Modeling and an Premature Contraction Arrhythmia Classification using Support Vector Machine

Auto Regressive모델링 기반의 특징점 추출과 Support Vector Machine을 통한 조기수축 부정맥 분류

  • Cho, Ik-sung (Daepartment of Creative Integrated General Studies, Daegu University) ;
  • Kwon, Hyeog-soong (Department of IT Engineering, Pusan National University) ;
  • Kim, Joo-man (Department of IT Engineering, Pusan National University) ;
  • Kim, Seon-jong (Department of IT Engineering, Pusan National University)
  • Received : 2018.10.17
  • Accepted : 2018.11.05
  • Published : 2019.02.28
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Abstract

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.

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경망, 퍼지, 시계열 주파수 분석, 비선형 분석법 등이 연구되어 왔다. 이러한 방법들은 분류율를 향상시키기 위해 정확한 특징점과 많은 양의 신호를 처리해야 하기 때문에 데이터의 가공 및 연산이 복잡하며, 다양한 부정맥을 분류하는데 어려움이 있다. 본 연구에서는 AR(Auto Regressive) 모델링 기반의 특징점 추출과 SVM(Support Vector Machine)을 통한 조기수축 부정맥 분류 방법을 제안한다. 이를 위해 잡음을 제거한 ECG 신호에서 R파를 검출하고 QRS와 RR 간격의 특정 파형 구간을 모델링하였다. 이후 최적 세그먼트 길이(n1, n2), 최적 차수( p1, p2)의 4가지 AR 모델링 변수를 추출하고 SVM을 통해 Normal, PVC, PAC를 분류하였다. 연구의 타당성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스를 대상으로 한 R파의 평균 검출 성능은 99.77%, Normal, PVC, PAC 부정맥은 각각 99.23%, 97.28, 96.62의 평균 분류율을 나타내었다.

Keywords

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Fig. 1 System configuration

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Fig. 2 Variability of QRS morphology by each arrhythmia

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Fig. 3 Variability of RR interval by each arrhythmia

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Fig. 4 Accuracy through QRS egment(n1=24), AR order (p1 : 2 ~ 9)

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Fig. 5 Accuracy through RR segment(n2=600), AR order(p2 : 2 - 9)

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Fig. 6 R peak detection & SVM classification result

Table. 1 Comparison of classification rates by parameters

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Table. 2 Arrhythmia classification rate

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Table. 3 Performance comparison between the proposed algorithm and state of the art papers

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