Deterministic Learning-Based WEST Syndrome Analysis and Seizure Detection on ECG.

WEST syndrome is an unknown etiology infant epilepsy, which is characterized by the flexion spastic seizure, intellectual motion development lag, electrode abnormalities, arrhythmia. In this brief, we present a novel electrocardiogram (ECG) based WEST syndrome epilepsy seizure detection method. Based on deterministic learning (DT) theory, the dynamic model of ECG is firstly constructed. The cardiodynamicsgrams (CDGs) of ECGs in seizure and interictal periods are then derived. Nonlinear features on CDGs are extracted for WEST syndrome characterization. For performance evaluation, experiments on ECGs of 12 WEST syndrome patients from the Children’s Hospital of Zhejiang University School of Medicine (CHZU) is carried out. The proposed method can obtain an average of $94.49{\%}$ F1-score, $93.76{\%}$ precision and $95.58{\%}$ accuracy, that outperforms the heart rate variability (HRV) based methods. [ABSTRACT FROM AUTHOR]