Algorithm for real-time prediction of neurally mediated syncope integrating indexes of autonomic modulation

Neurally mediated syncope (NMS) is a transient and self-limited loss of consciousness that affects all ages and is associated with high rates of falls and hospitalizations. In this study we propose a new algorithm for real-time prediction of NMS that integrates indexes of autonomic modulation among other parameters, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. ECG and PPG signals were acquired from 43 patients with suspected NMS, during scheduled diagnostic headup tilt table (HUTT) tests. Heart rate variability (HRV) indexes were integrated in a NMS prediction algorithm comprising surrogates of chronotropic, inotropic, blood pressure and vascular tone changes. The proposed algorithm was validated using a three-way data split approach. HRV indexes improved the algorithm performance in both the train/validation phase and the test phase, showing the importance of autonomic modulation indexes in real-time prediction of NMS.