A novel approach to classification of ECG arrhythmia types with latent ODEs
This enables smaller wearables for long-term monitoring of cardiac health by addressing the trade-off between signal fidelity and battery life, though it is incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of classifying ECG arrhythmia types from wearable devices with irregular, lower sampling frequencies by using latent ODEs to model continuous waveforms and gradient boosted trees for classification, achieving macro-averaged AUC-ROC values of 0.984, 0.976, and 0.976 at 360 Hz, 90 Hz, and 45 Hz, respectively, with minimal performance degradation.
12-lead ECGs with high sampling frequency are the clinical gold standard for arrhythmia detection, but their short-term, spot-check nature often misses intermittent events. Wearable ECGs enable long-term monitoring but suffer from irregular, lower sampling frequencies due to battery constraints, making morphology analysis challenging. We present an end-to-end classification pipeline to address these issues. We train a latent ODE to model continuous ECG waveforms and create robust feature vectors from high-frequency single-channel signals. We construct three latent vectors per waveform via downsampling the initial 360 Hz ECG to 90 Hz and 45 Hz. We then use a gradient boosted tree to classify these vectors and test robustness across frequencies. Performance shows minimal degradation, with macro-averaged AUC-ROC values of 0.984, 0.978, and 0.976 at 360 Hz, 90 Hz, and 45 Hz, respectively, suggesting a way to sidestep the trade-off between signal fidelity and battery life. This enables smaller wearables, promoting long-term monitoring of cardiac health.