Matthew C. Wiggins 1 , Edward P. Gerstenfeld 2 , George Vachtsevanos 1 , and Brian Litt 2

Electrogram Features are Superior to Clinical Characteristics for Predicting Atrial Fibrillation After Coronary Artery Bypass Graft Surgery Matthew C. Wiggins1, Edward P. Gerstenfeld2, George Vachtsevanos1, and Brian Litt2 1School of Electrical and Computer Engineering Georgia Institute of Technology, Atlanta, Georgia, USA. 2University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Research Motivation • Atrial Fibrillation (AF) • 2.2 million Americans • Increases risk of thromboembolism, stroke, and heart failure • 15% of strokes are associated with AF • 50% of AF associated strokes are major and disabling • Coronary Artery Bypass Graft (CABG) • 350,000 procedures annually in US • ~30% develop AF • $1 billion in the US annually

Hypothesis We hypothesized that ECG and intracardiac electrogram features would be superior to clinical characteristics in predicting the development of AF after CABG.

Data Set • 49 Patients undergoing CABG (16 develop AF) • Continuous ECG sampled at 250 Hz • 36 hours following surgery • Limb Lead – Modified Lead II • Atrial Wire • Non-ECG Features • Age • History of AF • β-Blocker withdrawal • Presence of chronic obstructive pulmonary disease • Missing Data Points

ECG Clips c1 c2 c3 c4 5 min 0 12 24 36 Hours After CABG

ECG Segments P-P P-R R-R R-P Atrial Lead P-P P-R R-R R-P Limb Lead

ECG Signal Features

ECG Segment Durations Atrial Lead P-to-P 1.02 s R-to-P 0.84 s P-to-R 0.18 s Chest Lead R-to-R 1.08 s

Segment Duration Features • Duration (P-P, P-R, R-R, R-P) • Frequency Decomposition • Lomb-Scargle Periodogram • Non-uniform spaced samples • Very Low Freq. (VLF) - 0.0033-0.4 Hz • Low Freq.(LF) - 0.04-0.15 Hz • High Freq. (HF) - 0.15-0.4 Hz • Total Power - 0-0.5 Hz • Ratios of the above

Statistics • Minimum • Maximum • Mean • Standard Deviation • Skewness • Kurtosis

Data Genetic Algorithm Features Winning kNN Classifier Evaluate Classifier Evolve Classifier Feature Matrix Patients Genetic Evolution

kNN Classifier • Determines class membership based on classes of the k (integer) nearest neighbors • Able to classify multimodal data • No ability to find p-value • Leave-one-out cross validation

Non-ECG based AF Risk Score • 4 variables • Age • History of AF • COPD • Beta-blocker withdrawal • 63.6% Accuracy (21 of 33) • Sensitivity of 90.9% and Specificity of 50% Mathew, J.P., et al., A multicenter risk index for atrial fibrillation after cardiac surgery. JAMA, 2004. 291(14): p. 1720-1729.

ECG basedAF Risk Score • 3 Neighbors • 2 variables from 36-hour segment • 90.9% (30 of 33) classification accuracy • Sensitivity of 82% and Specificity of 86% • ECG features only • Ectopic beats included

ECG basedAF Risk Score • 3 Neighbors • 2 variables • 89.5% (34 of 38) classification accuracy • Sensitivity of 76.9% and Specificity of 96% • ECG features only • Ectopic beats excluded

Summary • Clinical Feature Risk Score • Age, History of AF, COPD, and BB withdrawal • Accuracy of 63.6% (Sen=90.9%, Spec=50%) • ECG Feature Combinations • Min of the Median Freq. (Atrial RP segments 36 hours after surgery) & Skewness of the Heart Rate (36 hours after surgery) • Accuracy of 90.9% (Sen=82%, Spec=86%) • Max of the Peak Freq. (Limb PP segments 0 hours after surgery) & Kurtosis of the Peak Freq. (Atrial PR segments 24 hours after surgery) • Accuracy of 89.5% (Sen=76.9%, Spec=96%)

Conclusion • ECG based features offers information for risk stratification beyond clinical risk factors • Future work • Longer ECG segments • More patients • Perform analysis backward from AF onset • Apply criteria prospectively • Combine classical risk measures with ECG features

Matthew C. Wiggins 1 , Edward P. Gerstenfeld 2 , George Vachtsevanos 1 , and Brian Litt 2