ECG CLASSIFICATION FROM A SHORT SINGLE LEAD USING MACHINE LEARNING &

HAND-CRAFTED FEATURE EXTRACTIONHeather ShumakerECE 539 • Fall 2018

SUMMARYECG ClassificationNormal, Atrial Fibrillation (AF), Other rhythm, Noisy

• Signal processing

• Feature extraction

• Classifier training & testing

• Committee machine

75% average accuracy

DATA

60%

9%

30%

1%

8,528 ECG SAMPLESNormal AF Other Rhythm Noisy

PROPOSED APPROACH

FINAL APPROACH

Committee machine

1. Normal vs Abnormal classification

2. Abnormality classification

3. Combine results

FEATURE EXTRACTION

Atrial Fibrillation

Other Rhythm

Noisy

MATLAB Classification Learner

RESULTS

1,000 samples used for testing

Accuracy Normal vs. Abnormal (%) AF vs Other vs Noisy (%) Overall

Validation 82.2 80.2

Testing 80.9 61 74.6

DISCUSSIONRoom for improvement

• Uneven class distribution• Better solution for combining classifier outputs• More ECG features • Train neural network on features• Noisy data & inconsistent labeling

Lessons learned

• TensorFlow on laptop for predictions• Python-MATLAB interaction• MATLAB classification learner

QUESTIONS?