P and T wave analysis in ECG signals using Bayesian methods

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List of Figures 1.1 Structure diagram of the human heart. . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Blood flow diagram of the human heart. . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Heart electrical conduction system. . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 The Einthoven’s triangle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.5 Frontal plane limb leads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.6 The six standard precordial leads. . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.7 One depolarization / repolarization cycle. . . . . . . . . . . . . . . . . . . . . . 17 1.8 Normal features and the intervals of the ECG. . . . . . . . . . . . . . . . . . . 18 1.9 Normal sinus rhythm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.10 Sinus tachycardia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.11 Sinus bradycardia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.12 Sinus arrhythmia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.13 Atrial premature contractions. . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.14 Ventricular premature contractions. . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.15 Atrial fibrillation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.16 Ventricular fibrillation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.17 Ventricular escape beat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.18 Adaptive Filtering for ECG Baseline Removal. . . . . . . . . . . . . . . . . . . 25 1.19 QRS detection processing steps of Pan and Tompkins algorithm in [PT85]. . . 28 1.20 Results of the Pan’s QRS detection algorithm [PT85]. . . . . . . . . . . . . . . 28 1.21 Block diagram for the P and T wave delineation based on LPD. . . . . . . . . . 30 1.22 Detection of the peak and the end of a normal T wave by using the method [LTC + 90]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 1.23 WT at the first five scales of ECG-like simulated waves. . . . . . . . . . . . . . 33 1.24 Block diagram for the P and T wave delineation based on WT. . . . . . . . . . 34 1.25 Detection of a biphasic T wave boundaries by using its DWT. . . . . . . . . . . 34 1.26 Typical trajectory generated by the ECG dynamical model [MCTS03]. . . . . . 36 1.27 Five Gaussian functions with arrows indicating the kernels’ effect intervals. . . 36 1.28 Block diagram for the Gaussian mixed model-based delineation method. . . . . 37 2.1 Preprocessing procedure within the D-beat processing window . . . . . . . . . 42 2.2 Modeling of T wave parts within the T wave search blocks. . . . . . . . . . . . 43 2.3 Parameters of the wave delineation method. . . . . . . . . . . . . . . . . . . . . 55 2.4 Block diagram for the PCGS P and T wave delineation algorithm. . . . . . . . 56 2.5 Posterior distributions of the P and T wave indicator locations . . . . . . . . . 57 2.6 Posterior distributions of the P-wave amplitudes . . . . . . . . . . . . . . . . . 58 2.7 Dataset “sele0136” recovered from the estimates . . . . . . . . . . . . . . . . . 58 15
16 LIST OF FIGURES 2.8 Results of processing ECG signals from the QT database . . . . . . . . . . . . 60 2.9 Results of processing ECG signals from the QT database . . . . . . . . . . . . 61 2.10 ROC analysis for P and T wave detection. . . . . . . . . . . . . . . . . . . . . . 62 2.11 Evolution of MPSRF criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 2.12 Delineation results for QT database compared to the method of [SS09]. . . . . 64 2.13 Delineation results for QT database compared to the method of [SS09]. . . . . 65 2.14 Absolute error comparisons between the proposed method and the method of [SS09] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 2.15 Histograms of deviations compared to the “gold standard" . . . . . . . . . . . . 68 2.16 ECG signal within a D-beat window . . . . . . . . . . . . . . . . . . . . . . . . 70 2.17 Posterior distributions of the P and T wave indicator locations . . . . . . . . . 78 2.18 Dataset “sele0607” recovered from the estimates . . . . . . . . . . . . . . . . . 78 2.19 Posterior distributions of the P and T wave indicator locations. . . . . . . . . . 79 2.20 Dataset “sel803” recovered from the estimates . . . . . . . . . . . . . . . . . . . 79 3.1 Signal model for the beat-to-beat processing scheme. . . . . . . . . . . . . . . . 85 3.2 Delineation results obtained for three different T wave morphologies. . . . . . . 92 3.3 Posterior distributions obtained by using the beat-to-beat block Gibbs sampler. 94 3.4 Non-QRS component estimation comparison of QTDB sele0136 in noisy conditions. 96 3.5 Non-QRS component estimation comparison of QTDB sel803 in noisy conditions. 97 3.6 Signal model within a non-QRS interval. . . . . . . . . . . . . . . . . . . . . . . 101 3.7 An example of the boundary problem with PVC signal. . . . . . . . . . . . . . 109 3.8 A new boundary definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 3.9 Estimation performance versus the number of particles. . . . . . . . . . . . . . 110 3.10 Non-QRS component estimation of QTDB sele0136 obtained by the beat-to-beat block Gibbs sampler and the MPF. . . . . . . . . . . . . . . . . . . . . . . . . . 112 3.11 Non-QRS component estimation of QTDB sel803 obtained by the beat-to-beat block Gibbs sampler and the MPF. . . . . . . . . . . . . . . . . . . . . . . . . . 113 4.1 Visible TWA in ECG signals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 4.2 General TWA preprocessing stage. . . . . . . . . . . . . . . . . . . . . . . . . . 120 4.3 Signal model for TWA Bayesian analysis. . . . . . . . . . . . . . . . . . . . . . 126 4.4 TWA synthesizer scheme with real ECG signal. . . . . . . . . . . . . . . . . . . 130 4.5 Estimation results for an ECG signal segment with synthetic TWA. . . . . . . 132 4.6 The KS-test and the t-test decisions made for three different 16-beat windows. 133 4.7 Detection performance for real ECGs with synthetic TWA. . . . . . . . . . . . 134 4.8 An example of selected EGM episode signal. . . . . . . . . . . . . . . . . . . . . 138 4.9 An example of EGM reference signal. . . . . . . . . . . . . . . . . . . . . . . . . 138 4.10 T wave landmarks calculated for each heartbeat. . . . . . . . . . . . . . . . . . 141 4.11 Schematic representation of the T wave parameters. . . . . . . . . . . . . . . . 142 4.12 General block diagram for the beat-to-beat TWA detection in EGM signals. . . 144 4.13 One EGM portion with synthetic TWA. . . . . . . . . . . . . . . . . . . . . . . 145 4.14 Fisher score of each parameter calculated on Beat-to-beat variations of reference and synthesized 50µv TWA data. . . . . . . . . . . . . . . . . . . . . . . . . . . 146 4.15 Fisher score of each parameter calculated on beat-to-beat variations of reference and synthesized 25µv TWA data. . . . . . . . . . . . . . . . . . . . . . . . . . . 147 4.16 Beat-to-beat waveform estimation on a real EGM reference signal. . . . . . . . 149 4.17 Beat-to-beat waveform estimation on a real EGM episode signal. . . . . . . . . 150
Page 1 and 2: InstitutNationalPolytechniquedeToul
Page 3 and 4: 2 Armengaud, Sylvie Eichen, Marie-J
Page 6: Résumé Cette thèse a pour objet
Page 10: 9 ROC SA SM SMC SNR SPF ST STFT TWA
Page 13 and 14: 12 CONTENTS 2.4.1 Hermite basis dec
Page 15: 14 CONTENTS
Page 19 and 20: 18 LIST OF FIGURES
Page 21 and 22: 20 LIST OF TABLES
Page 23 and 24: 22 LIST OF ALGORITHMS
Page 25 and 26: 2 General Introduction fluctuation,
Page 27 and 28: 4 General Introduction • Chapter
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Page 31 and 32: 8 Chapter 1 - Introduction to Cardi
Page 33 and 34: 10 Chapter 1 - Introduction to Card
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160 Conclusions and Perspectives of
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162 Conclusions and Perspectives
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Appendix A Sampling distributions f
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167 Waveform coefficients. The samp
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Appendix B Sampling distributions f
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171 that can be summarized as [ ∝
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Appendix C Sampling distributions f
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175 Inserting (2.5) for p(x|θ) and
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Appendix D Proposal distributions f
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Appendix E T waveform estimation us
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181 that the noise variance σ 2 w,
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Bibliography [AAC81] [AAC + 04] [Al
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BIBLIOGRAPHY 185 [CM08] [CPD09] [CR
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BIBLIOGRAPHY 187 [Har78] [HB97] [he
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BIBLIOGRAPHY 189 [MHI + 05] A. Mayb
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BIBLIOGRAPHY 191 [SGN05] [SLKG02] [
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P and T wave analysis in ECG signals using Bayesian methods

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