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

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 monitorTraditional 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 # of patientsSensitivityPositive predictivity 1199.57 % 999.95 %99.92 %

14. QRS Detection Results – Standard Database Detection results compared to other studies using a 2 channel approach to automatic QRS complex detection: StudySensitivityPositive predictivity This work99.63 % Ghaffari et al.99.94 %99.91 % Boqiang et al.99.91%99.93 % Chiarugi et al.99.76 %99.81 %

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