Early-Fusion Two-Channel ECG Scalograms and Transfer Learning for Record-Level Classification of Arrhythmia, Congestive Heart Failure and Normal Sinus Rhythm

Esfandiar Khaleghi, Olga Duran, Iosif Mporas, A.T. Augousti

Automatic electrocardiogram (ECG) classification using deep learning is sensitive to data leakage, class imbalance and the way multi-segment decisions are aggregated at record level. This study presents a two-channel time–frequency early-fusion pipeline for classifying ECG recordings into arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythm (NSR). The curated PhysioNet-derived ECGData dataset was used, comprising 81 reconstructed two-channel records: 48 ARR, 15 CHF and 18 NSR. Each 512 s ECG record, sampled at 128 Hz, was segmented into 10 s segments with 50% overlap. Continuous wavelet transform scalograms were generated for both ECG channels and horizontally fused into a single image representation for transfer learning with GoogLeNet. Signal-domain augmentation and class-weighted training were applied to improve robustness to small recording variations and class imbalance. To reduce leakage from overlapping segments, all segments from the same record were kept within the same fold during five-fold record-level cross-validation. Record-level predictions were obtained by averaging posterior probabilities across all segments from each record. The proposed early-fusion model achieved 94.83% mean segment-level accuracy and 98.77% aggregate record-level accuracy, correctly classifying 80 of 81 records. Under the same folds and training configuration, single-channel baselines achieved 75.31% and 87.65% aggregate record-level accuracy, indicating that two-channel early fusion improved classification reliability over single-channel ECG modelling. The only record-level error was a CHF recording classified as ARR. Although the results are promising, the CHF class contained only 15 records and the diagnostic classes originated from different source databases; therefore, further validation on larger, clinically verified multi-lead ECG datasets is required.