Medical Engineering & Physics
Volume 31, Issue 10 , Pages 1219-1227, December 2009

A robust wavelet-based multi-lead electrocardiogram delineation algorithm

  • A. Ghaffari

      Affiliations

    • Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran
    • CardioVascular Research Group (CVRG), Iran
  • ,
  • M.R. Homaeinezhad

      Affiliations

    • Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran
    • CardioVascular Research Group (CVRG), Iran
    • Corresponding Author InformationCorresponding author at: Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
  • ,
  • M. Akraminia

      Affiliations

    • Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran
    • CardioVascular Research Group (CVRG), Iran
  • ,
  • M. Atarod

      Affiliations

    • Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran
    • CardioVascular Research Group (CVRG), Iran
  • ,
  • M. Daevaeiha

      Affiliations

    • Non-invasive Cardiac Electrophysiology Laboratory, DAY Hospital, Tehran, Iran

Received 19 April 2009; received in revised form 22 July 2009; accepted 23 July 2009. published online 19 August 2009.

Abstract 

A robust multi-lead ECG wave detection-delineation algorithm is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting QRS complex, P-wave and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). First, a window with a specific length is slid sample to sample on the selected scale and the curve length in each window is multiplied by the area under the absolute value of the curve. In the next step, a variable thresholding criterion is designed for the resulted signal. The presented algorithm is applied to various databases including MIT-BIH arrhythmia database, European ST-T Database, QT Database, CinC Challenge 2008 Database as well as high resolution Holter data of DAY Hospital. As a result, the average values of sensitivity and positive predictivity Se=99.84% and P+=99.80% were obtained for the detection of QRS complexes, with the average maximum delineation error of 13.7ms, 11.3ms and 14.0ms for P-wave, QRS complex and T-wave, respectively. The presented algorithm has considerable capability in cases of low signal-to-noise ratio, high baseline wander, and abnormal morphologies. Especially, the high capability of the algorithm in the detection of the critical points of the ECG signal, i.e. the beginning and end of T-wave and the end of the QRS complex was validated by cardiologists in DAY hospital and the maximum values of 16.4ms and 15.9ms were achieved as absolute offset error of localization, respectively.

Abbreviations: ACL, area-curve length, ECG, electrocardiogram, DWT, discrete wavelet transform, QTDB, QT database, MITDB, MIT-BIH arrhythmia database, TWADB, T-wave alternans database, CSEDB, common standards for electrocardiography database, EDB, European ST-T database, P+, positive predictivity (%), Se, sensitivity (%), FIR, finite-duration impulse response, LE, location error, CHECK#0, procedure of evaluating obtained results using MIT annotation files, CHECK#1, procedure of evaluating obtained results consulting with a control cardiologist, CHECK#2, procedure of evaluating obtained results consulting with a control cardiologist and also at least with 3 residents

Keywords: ECG delineation, Discrete wavelet transform, Variable threshold, Validation

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PII: S1350-4533(09)00164-7

doi:10.1016/j.medengphy.2009.07.017

Medical Engineering & Physics
Volume 31, Issue 10 , Pages 1219-1227, December 2009