4つの電極を用いた解剖学的に根拠のある合成リードによる心電図再構成：ハイブリッドCNN-トランスフォーマーマッピングによる生理学的測定フレームワーク

The standard 12-lead electrocardiogram (ECG) remains essential for cardiac diagnosis but requires ten physical electrodes, limiting long-term and wearable monitoring applications. We developed an anatomically grounded and physiologically interpretable framework to reconstruct the complete 12-lead ECG from four synthetic chest-torso electrodes derived using geometric vector principles and cardiac territorial anatomy. Approach: The 12 standard leads were partitioned into four physiologically coherent clusters representing septal/anterior, apical-lateral, inferior, and high-lateral depolarization vectors. Synthetic electrodes were constructed as weighted linear combinations of standard leads guided by frontal- and horizontal-plane vector geometry. A hybrid convolutional neural network-Transformer architecture mapped these four synthetic inputs to full 12-lead waveforms. The model was trained on 21,786 recordings from the PTB-XL dataset and externally validated on 500 recordings from the Chapman-Shaoxing dataset. Performance was evaluated using coefficient of determination (R²), Pearson correlation, root mean square error (RMSE), diagnostic concordance analysis, ablation testing, and noise robustness assessment. Main results: On the internal test set, the model achieved mean R² = 0.878 ± 0.070, Pearson correlation ρ = 0.939 ± 0.030, and RMSE = 0.071 ± 0.030 mV. External validation demonstrated only 5% performance degradation. Waveform component preservation exceeded 94%, ST-segment correlation reached 0.964, and overall diagnostic concordance was 0.883, indicating preservation of approximately 88% of clinically relevant information. Reconstruction errors were symmetrically distributed around zero with minimal bias (0.001 mV) and maintained robustness at signal-to-noise ratios ≥ 10 dB. Significance: This anatomically explainable reconstruction framework demonstrates the algorithmic feasibility of compact four-electrode ECG systems while preserving high diagnostic fidelity. By grounding electrode design in cardiac vector anatomy and validating performance across datasets, the approach provides a physiologically interpretable foundation for future wearable and ambulatory ECG reconstruction systems, establishing a reconstruction ceiling prior to hardware implementation.