Abstract 4144676: Leveraging AI to Identify ECG Indicators of Ventricular Dysfunction

Introduction: Recent studies have demonstrated that artificial intelligence can effectively identify ventricular dysfunction using electrocardiograms (ECGs). However, the specific ECG waveforms indicative of left ventricular dysfunction remain poorly understood. This study aims to identify the ECG leads and segments that most accurately signal left ventricular dysfunction. Methods: We utilized ECG and echocardiography datasets comprising 17,422 cases from Japan and Germany. Convolutional neural networks with ten layers were developed to detect left ventricular ejection fractions below 50%. The models were trained and validated through 4-fold cross-validation using the Japanese dataset. The performance of the models was evaluated and compared among various ECG configurations (3-second strips, single-beat, and adjacent 2-beat overlay) and ECG segments (PQRST, QRST, P, QRS, PQRS). In addition to internal validation using Japanese data not employed during training, external validation was conducted with the German dataset. Results: Models based on 2-beat ECGs significantly outperformed the other models. Models with single-beat ECGs showed at least equivalent to superior performance compared to the 3-second models in both internal and external validations (AUC 0.908±0.001, 0.863±0.001 vs 0.826±0.016, respectively for 2-beat, single-beat, and 3-second models, P<0.0001, external validation). Employing single-beat models, our analysis found that limb leads, particularly leads I and aVR, were more indicative of left ventricular dysfunction. Furthermore, within the single-beat ECGs, the segments from QRS to T-wave were most revealing, while the addition of P segments significantly augmented the models’ performance ( Fig.1 ). Conclusions: This study confirms that using ECG segments from the P to the T-wave is more effective for assessing ventricular dysfunction than 3-second ECG strips, with performance further enhanced by multiple heartbeats. Notably, signals indicating ventricular dysfunction were predominantly distributed within the QRS to T-wave segments, while the P-segment also showed some significance. The concentration of dysfunction signals varied by lead, with leads I and aVR providing higher diagnostic utility. Further research is needed to identify specific ECG signals or waveforms for ECG-based diagnostics of ventricular dysfunction.