QRS Cancellation

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Transcript of QRS Cancellation

  • Contents

    1 Introduction 1

    2 Medical Background 32.1 Anatomy of the Human Heart . . . . . . . . . . . . . . . . . . . . 32.2 Atrial fibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

    2.2.1 Electrical Activity in NSR and in AF . . . . . . . . . . . 52.2.2 Classification of AF . . . . . . . . . . . . . . . . . . . . . 6

    2.3 ECG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.1 Formation of the ECG Signal . . . . . . . . . . . . . . . . 82.3.2 ECG in Normal Sinus Rhythm and Fibrillatory Rhythm . 10

    3 Methodology 133.1 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Noise Level Estimation . . . . . . . . . . . . . . . . . . . . . . . . 143.3 The Pan-Tompkins algorithm for QRS detection . . . . . . . . . 153.4 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 16

    3.4.1 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . 193.5 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

    3.5.1 Bayes Decision Theory . . . . . . . . . . . . . . . . . . . . 193.5.2 Artificial Neural Network . . . . . . . . . . . . . . . . . . 213.5.3 K Nearest Neighbor Classifier . . . . . . . . . . . . . . . . 23

    3.6 Post-processing and Diagnostic Decision . . . . . . . . . . . . . . 24

    4 QRST Cancellation 274.1 Straight Forward Averaging Algorithm for QRST Cancellation . 274.2 Improved QRST Cancellation . . . . . . . . . . . . . . . . . . . . 30

    4.2.1 Digital Filters . . . . . . . . . . . . . . . . . . . . . . . . . 314.2.2 QRS Clustering . . . . . . . . . . . . . . . . . . . . . . . . 334.2.3 Sub-clustering based analysis on RR-interval . . . . . . . 384.2.4 Appropriate Templates and Subtraction . . . . . . . . . . 394.2.5 Frequency Spectrum of AF . . . . . . . . . . . . . . . . . 444.2.6 Fourier Transform and Power Spectrum . . . . . . . . . . 45

    5 Results and Discussion 495.1 Results on Belt Database . . . . . . . . . . . . . . . . . . . . . . 495.2 Results on MIT Database . . . . . . . . . . . . . . . . . . . . . . 555.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

    5.3.1 Limitation of QRST Cancellation . . . . . . . . . . . . . . 58

  • ii Contents

    5.4 The first and the beat window II . . . . . . . . . . . . . . . . . . 625.5 QRS clustering Methods . . . . . . . . . . . . . . . . . . . . . . . 625.6 Survey of QRST cancellation using appropriate templates . . . . 645.7 Results summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

    6 Summary and Perspective 71

    A Abbreviations and Acronyms 73

    B COOKING BOOK FOR AF DETECTION TOOLBOX 75

    C The M-file structure of AF detection using MIT [1] and OWNdatabase 81

    References 85

  • List of Figures

    2.1 The cardiac conduction system . . . . . . . . . . . . . . . . . . . 42.2 Diagram of electrical activity in NSR and during atrial fibrillation 52.3 Representative human ECG waveform . . . . . . . . . . . . . . . 72.4 The generation of the ECG signal in the Einthoven limb leads. . 92.5 ECG in sinus rhythm and fibrillatory rhythm . . . . . . . . . . . 10

    3.1 A example from the MIT-BIH AF database . . . . . . . . . . . . 133.2 ECG miniature monitor and traces . . . . . . . . . . . . . . . . . 143.3 Noise Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.4 Block diagram of the Pan-Tompkins algorithm for QRS detection 153.5 FeatureExctraction . . . . . . . . . . . . . . . . . . . . . . . . . . 173.6 DecisionTree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.7 A neuron with a single scalar input and bias . . . . . . . . . . . . 223.8 layers of back propagation . . . . . . . . . . . . . . . . . . . . . . 223.9 An example of 5-Nearest Neighbor classifier . . . . . . . . . . . . 243.10 Post-processing classifier output using moving non-overlapping

    window containing 6 beats in this example. . . . . . . . . . . . . 253.11 Example of receiver operating characteristic curve . . . . . . . . 26

    4.1 Example of AF episode acquired by wearable belt system . . . . 274.2 ECG and their respective remainder ECG are shown for an ex-

    ample of NSR and AF . . . . . . . . . . . . . . . . . . . . . . . . 284.3 ECG Example, uniform template and its respective residual sig-

    nal after subtraction . . . . . . . . . . . . . . . . . . . . . . . . . 294.4 Flow chart of QT cancellation algorithm. . . . . . . . . . . . . . 304.5 Frequency response of the 50 Hz low-pass filter . . . . . . . . . . 324.6 Frequency response of the 50 Hz Notch filter . . . . . . . . . . . 334.7 Noisy ECG signal and filtered ECG signal . . . . . . . . . . . . . 344.8 Schematic representation of QRS clustering . . . . . . . . . . . . 364.9 Result of QRS clustering for the ECG example in the Fig. 4.3 . . 374.10 The appropriate templates superimposed on heart beats of each

    cluster or subgroup for ECG example in Fig. 4.3 . . . . . . . . . 384.11 Flow chart of computing appropriate templates . . . . . . . . . . 394.12 An AF example, appropriate templates for QRST Cancellation

    and its respective residual signal . . . . . . . . . . . . . . . . . . 404.13 Remainder of the AF Example using the forward averaging al-

    gorithm and improved QRST Cancellation. . . . . . . . . . . . . 41

  • iv List of Figures

    4.14 An NSR example, template for QRST Cancellation and its re-spective residual signal . . . . . . . . . . . . . . . . . . . . . . . . 42

    4.15 Definition of beat window . . . . . . . . . . . . . . . . . . . . . . 434.16 Frequency spectra from the residual signals in the AF and non-

    AF cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

    5.1 QT features averaged over AF and NSR records . . . . . . . . . . 515.2 Histogram of QTCan14 and QTCan11 . . . . . . . . . . . . . . . 525.3 ROC curve for combining features of QTCan14 and RR interval 545.4 Plot features of AF and NSR episode from record 04746 . . . . . 565.5 AF example with not apparent fibrillatory waves . . . . . . . . . 595.6 NSR example with chaotic atrial activity. . . . . . . . . . . . . . 605.7 Noisy ECG signal causes considerable error in QRST cancellation. 605.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.9 K-mean clustering partitions the QRS complexes into three clusters 635.10 Frequency spectrum of the entire P wave . . . . . . . . . . . . . . 655.11 Decision curves comparison . . . . . . . . . . . . . . . . . . . . . 665.12 Performance comparision . . . . . . . . . . . . . . . . . . . . . . 675.13 QDC Density Estimation . . . . . . . . . . . . . . . . . . . . . . 675.14 Density function . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.15 Decision curves comparison . . . . . . . . . . . . . . . . . . . . . 695.16 QDC Density Estimation . . . . . . . . . . . . . . . . . . . . . . 695.17 Density function . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

  • List of Tables

    2.1 Classification of AF . . . . . . . . . . . . . . . . . . . . . . . . . 6

    5.1 Features of QRST Cancellation of chronic AF patients for eachrecord . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

    5.2 Features of QRST Cancellation of NSR patients for each record . 505.3 Averaging features for AF and NSR records . . . . . . . . . . . . 515.4 Results of AF detection using QRST Cancellation features on

    wearable belt system. . . . . . . . . . . . . . . . . . . . . . . . . 535.5 Results of AF detection using features of RR interval and QRST

    Cancellation on wearable belt system. . . . . . . . . . . . . . . . 545.6 The duration of annotated segment in minutes and the number

    of heart beats for each MIT AF database . . . . . . . . . . . . . 575.7 AF detection using feature of RR interval, QRST Cancellation

    and combining the both features on MIT database. . . . . . . . . 585.8 AF detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665.9 AF detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

  • 1 Introduction

    Atrial fibrillation (AF) is a common arrhythmia with a prevalence of approxi-mately 0.4-1% in the general population. Prevalence increases with age and itis estimated to be present in 5% of those older than 65, and 10% of those olderthan 70. It is associated with an increased risk of stroke and mortality, as wellas congestive heart failure and cardio-myopathy. AF gives rise to a significantincrease in mortality [2].

    To help in the fight against AF disease, a system is developed for the contin-uous monitoring of health status based on non-invasive wearable sensors inte-grated in the Philips strap. The disease knowledge is enriched over time as thesystem learns the patients behavior, for example, by monitoring and remem-bering the heartbeat during daily activities. Biometric signals, primarily ECG(electrocardiogram), are collected in the hospital or at home and displayed ona central station.

    ECG signal, which is a graphical representation of the potential differencesmeasured between two points on body surface versus time, is produced byactivation front of cardiac depolarization and repolarization. ECG signals arelargely employed as a diagnostic tool in clinical practice in order to assess thecardiac status of the patient. As one of the most important pieces of vitalinformation, the ECG signal plays an important role in the continuous patientmonitoring for the people, who suffer from chronic cardiovascular diseases. Thisabnormal excitation propagation of AF patients results in morphology changesin ECG. The ECG of AF patients is characterized by irregular RR intervalscaused by chaotic atrial depolarization waves penetrating the AV node in anirregular manner. We can not see any consistent P wave due to chaotic atrialactivi