An ECG early prediction algorithm integrating autonomic imbalance and repolarization energospectral shift

Syncope is a transient loss of consciousness and increased fall risk due to cerebral hypoperfusion, often triggered by prolonged upright posture. Clinically, differential diagnosis largely relies on clinical manifestations and haemodynamic responses observed during the head-up tilt test (HUTT), whereas prediction is typically based on electrocardiographic information acquired during HUTT. However, early identification remains challenging because prodromal symptoms are often nonspecific and HUTT is time-consuming and may cause patient discomfort.

A novel detection framework, AReS-Syncope, was designed to facilitate early syncope prediction by integrating metrics of autonomic imbalance and repolarizationâ€"energospectral alterations. Two feature sets were established: Autonomic Imbalance Features (AIF), capturing RR-interval prolongation and irregular variability preceding syncope and Repolarizationâ€"Energospectral Shift Features (RES), quantifying reduced repolarization reserve and a shift in spectral energy toward lower frequencies during postural stress and autonomic dysregulation. The dimensionally reduced AIF and RES sets were fed into a support vector machine classifier, and stability was evaluated across prediction windows of varying durations.

The AReS-Syncope algorithm achieved early prediction of syncope using ECG signals alone, with a prediction horizon of 80 s before syncope. Using patient-level nested five-fold cross-validation, the model achieved an AUC of 91.42%, an accuracy of 84.60%, a sensitivity of 86.39%, and a specificity of 82.62%.

The AReS-Syncope algorithm uses ECG to identify autonomic withdrawal and repolarization abnormalities before syncope, enabling early prediction and providing a viable tool for effective patient monitoring.