Phase space reconstruction approach for ventricular arrhythmias characterization

Ventricular arrhythmias, especially tachycardia and fibrillation are one of the main causes of sudden cardiac death. Therefore, the development of methodologies, enable to detect their occurrence and to characterize their time evolution, is of fundamental importance. This work proposes a non-linear dynamic signal processing approach to address the problem. Based on the phase space reconstruction of the electrocardiogram (ECG), some features are extracted for each ECG time window. Features from current and previous time windows are provided to a dynamic neural network classifier, enabling arrhythmias detection and evolution trends assessment. Sensitivity and specificity values, evaluated from public MIT-BIH databases, show the effectiveness of the proposed strategy.

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