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Automatic QRS Complex Detection Algorithm Designed for a Novel Electrocardiogram Recording Device

Automatic QRS Complex Detection Algorithm Designed for a Novel Electrocardiogram Recording Device. Dorthe Bodholt Nielsen, Ph.D. student, DELTA / Technical University of Denmark Contact: dbn@delta.dk. Co-authors Kenneth Egstrup, OUH Svendborg Hospital Jens Branebjerg, DELTA

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Automatic QRS Complex Detection Algorithm Designed for a Novel Electrocardiogram Recording Device

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  1. Automatic QRS Complex Detection Algorithm Designed for a Novel Electrocardiogram Recording Device Dorthe Bodholt Nielsen, Ph.D. student, DELTA / Technical University of Denmark Contact: dbn@delta.dk Co-authors Kenneth Egstrup, OUH Svendborg Hospital Jens Branebjerg, DELTA Gunnar Bjarne Andersen, DELTA Helge B. D. Sørensen, Technical University of Denmark

  2. Agenda • Application Example: Atrial Fibrillation • Advantages of our wireless ePatch technology • Algorithm: Automatic QRS complex detection • Detection Results • Conclusions and Future Work

  3. The Heart and ECG Signals Reference: http://elf.cs.pub.ro/pm/wiki/eestec/3

  4. Atrial Fibrillation (AF) • Definition: • Irregular and very fast activation of the atria • Irregular and fast pulse (ventricular contractions) • Prevalence: • 1 – 2 % of the general population • The prevalence increases with age: • 5 – 15 % at the age of 80 years • Progression of disease: • Paroxysmal → persistent → permanent • Symptoms • Palpitations (“hjertebanken”) • Dyspnoea • No symptoms

  5. Atrial Fibrillation • Adverse clinical events • heart failure • Death rate is doubled • Risk of stroke is 5-fold compared to general population • Treatment of AF • Stroke prophylaxis with anticoagulation therapy • Importance of early detection of AF • It is very important to diagnose patients with AF early to start anticoagulation treatment and decrease stroke risk. • Asymptomatic patients: Screening for AF in the general population or high risk groups. • Paroxysmal AF: Very long term monitoring might be needed to find an episode of AF and diagnose the patient.

  6. Advantages of the ePatch Heart Monitor • The ePatch heart monitor Traditional HOLTER monitor http://flightphysical.com/Exam-Guide/CV/Holter-Monitor.htm

  7. Automatic AF Detection • Embedded implementation of automatic signal processing algorithms for detection of cardiac arrhythmias, like atrial fibrillation. Hardware implementation of automatic ECG arrhythmia detection algorithms

  8. Atrial Fibrillation in ECG Signals • Definition of AF in ECG signals • Surface ECG shows irregular RR intervals • Surface ECG shows no distinct P waves • The interval between two atrial activations is usually variable and <200ms • Example of AF recorded with the ePatch heart monitor: • Example of normal ECG recorded with the ePatch heart monitor:

  9. Step I: Detection of Heart Beats • Automatic AF detection algorithms in the literature have three different approaches for automatic AF detection: • Detection based on the irregular RR intervals • Detection based on the absence of P-waves • Detection based on both irregular RR intervals and absence of P-waves • In order to apply either of these, it is necessary to design an automatic QRS complex detection algorithm.

  10. Automatic QRS Complex Detection • Schematic illustration of the designed automatic QRS complex detection algorithm:

  11. Automatic QRS Complex Detection Raw ECG, Lead I Feature I, Lead I Adaptive thresholding, Feature I, Lead I Binary feature signal, Feature I, Lead I Final QRS position

  12. Databases • The ePatch database: • 30 minute records from 11 different patients • Manual annotation of more than 22,000 heart beats • The MIT-BIH Arrhythmia Database (standard database) • 30 minute records from 48 different patients • Manual annotation of more than 91,000 heart beats

  13. QRS Detection Results – ePatch database • Performance measures: • Sensitivity = TP/(TP + FN) • Positive predictivity = TP/(TP + FP) • QRS detection performance: • All abnormal beats were correctly detected by the algorithm

  14. QRS Detection Results – Standard Database • Detection results compared to other studies using a 2 channel approach to automatic QRS complex detection:

  15. Conclusions and Future Work • Promising performance: • The algorithm should, of course, be evaluated on a larger ePatch database • This algorithm might be applied to initiate different arrhythmia detection algorithms that rely on the detection of heart beats. • Our current work is to design new algorithms for automatic detection of critical heart arrhythmias, like atrial fibrillation.

  16. Thank you for listening... • Questions and comments are very welcome! • Contact: dbn@delta.dk

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