Detection and prediction of the onset of human ventricular fibrillation: an approach based on complex network theory

Ventricular fibrillation is a life-threatening cardiac arrhythmia which deserves quick and reliable detection as well as prediction from human electrocardiogram time series. We constructed networks of human ventricular time series with the visibility graph approach to study the network subgraph phenomenon and motif ranks. Our results show that different dominant motifs exist as an effective indicator in distinguishing ventricular fibrillations from normal sinus rhythms of a subject. We verify the reliability of our findings in a large database with different time lengths and sampling frequencies, and design an onset predictor of ventricular fibrillations with reliable verifications.