한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지

PVC 분류를 위한 적응형 문턱치와 윈도우 기반의 R파 검출 알고리즘

R Wave Detection Algorithm Based Adaptive Variable Threshold and Window for PVC Classification

  • 조익성 (부산대학교 바이오정보전자공학과) ;
  • 권혁숭 (부산대학교 바이오메디컬공학과)
  • 발행 : 2009.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

조기심실수축(premature ventricular contractions, PVC)은 가장 보편적인 부정맥으로 심실세동, 심실빈맥 등과 같은 위험한 상황을 유발할 수 있는 가능성을 가지고 있기 때문에 이의 조기 검출은 매우 중요하다. 특히 일반인들의 건강상태를 지속적으로 모니터링 해야하는 헬스케어 시스템에서는 이를 위한 ECG 신호의 실시간 처리가 필요하다. 즉, 최소한의 연산량으로 정확한 R파를 검출하고, 이를 이용하여 PVC를 분류할 수 있는 적합한 알고리즘의 설계가 필요하다. 따라서 본 연구에서는 PVC 실시간 분류를 위한 적응형 문턱치와 윈도우 기반의 R파 검출 알고리즘을 제안한다. 이를 위해 전처리 과정과 적응가변형 문턱치를 통해 R파를 검출하였으며, 검출의 효율성을 위하여 R-R 간격을 이용한 적응가변형 윈도우를 적용하였다. 제안한 알고리즘의 R파 검출 및 PVC 분류 성능을 평가하기 위해서 MIT-BIH 부정맥 데이터베이스를 사용하였다. 성능평가 결과, R파는 평균 99.33%, PVC는 평균 88.86%의 검출결과가 나타났다.

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

키워드

참고문헌

  1. S. Sangwatanaroj, S. Prechawat, B.Sunsaneewitayakul, S. Sitthisook, P.Tosukhowong, and K. Tungsanga, 'New electrocardiographic leads and the procainamide test for the detection of the Brugada sign in sudden unexplained death syndrome survivors and their relatives,' Eur. Heart J., Vol. 22, no.24, pp.2290-2296, 2001 https://doi.org/10.1053/euhj.2001.2691
  2. S. F.Wung and B. Drew, 'Comparison of 18-lead ECG and selected body surfacepotential mapping leads in determiningmaximally deviated ST lead and efficacy in detecting acute myocardial ischemia during coronary occlusion,' J. Electrocardiol., vol. 32,pp. 30-37, 1999 https://doi.org/10.1016/S0022-0736(99)90032-8
  3. Beuchee A, Pladys P, Senhadji L, Betremieux P, Carre F. 'Beat-to-beat blood pressure variability and patent ductus arteriosus in ventilated, premature infants', Pflugers Arch, 2003, 446:154-160 https://doi.org/10.1007/s00424-002-0961-3
  4. Awdah Al-Hazimi, Nabil Al-Ama, Ahmad Syiamic, Reem Qosti, and Khidir Abdel-Galil,'Time domain analysis of heart rate variability in diabetic patients with and without autonomic neuropathy,' Annals of Saudi Medicine, 22(5-6), 2002, pp.400-402 https://doi.org/10.5144/0256-4947.2002.400
  5. P. Zhou, B. Lock, and T. A. Kuiken, 'Real time ECG artifact removal for myoelectric prosthesis control,' Physiological Measurement, Vol.28,pp.397.413, 2007 https://doi.org/10.1088/0967-3334/28/4/006
  6. D. Cypher, N. Chevrollier, N. Montavont, and N. Golmie, 'Prevailing over wires in healthcare environments: benefits and challenges,' IEEE Commun. Mag., Vol.44, No.4, pp.56.63, 2006 https://doi.org/10.1109/MCOM.2006.1632650
  7. Pan J. and J. Tompkins, 'A Real-Time QRS Detection Algorithm', IEEE Transactions on Biomedical Engineering, 32, 230-236, 1985 https://doi.org/10.1109/TBME.1985.325532
  8. Urrusti JL, Tompkins WJ. 'Performance evaluation of an ECG QRS complex detection algorithm,' Proc. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1993:800-801
  9. Peretz A, Kaufman J, Trenga C, Allen. J,Carlsten C., Aulet M., Adar S, Sullivan J,'Effects of diesel exhaust inhalation on heart rate variability in human volunteers,'Environmental Research, Vol.107, issue 2, pp.178-184 https://doi.org/10.1016/j.envres.2008.01.012
  10. Brignole M, Bellardine Black CL, Thomsen PE,Sutton R, Moya A, Stadler RW, Cao J, 'Messier M, Huikuri HV, Improved arrhythmia detection in implantable loop recorders,' J Cardiovasc Electrophysiol. 2008 Sep;19(9):928-34 https://doi.org/10.1111/j.1540-8167.2008.01156.x
  11. S. Gaitanidou, S. Rokas, C. Pamboucas, D.Actipis, S. Chatzidou, J. Darsinos, S. Stamatelopoulos,and S. Moulopoulos, 'The RR intervaldistribution pattern as a predictive factor of the outcome of atrioventricular conduction modification in patients with atrial fibrillation,' J. Amer. Coll. Cardiol., Vol.31, No.2, pp. 333A-334A, Feb. 1998 https://doi.org/10.1016/S0735-1097(98)82098-6
  12. P. de Chazal and R. Reilly. A patient-adapting heartbeat classifier using ecg morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 53(12): 2535-2543,2006 https://doi.org/10.1109/TBME.2006.883802