An Innovative Method to Distinguish Chaos from Noise in the Time Domain

Electrocardiogram (ECG) signals present significant challenges for classification due to their complex and variable nature. This study introduces a novel approach for distinguishing between normal and atrial ECG signals by utilizing a geometric feature space (GFS) generated from three specific geometric characteristics: amplitude, zenith angle, and shape factor. By employing a sweeping technique to extract and compile distinctive information, the proposed algorithm constructs this feature space, which is then processed using standard machine learning models for classification. Our methodology demonstrates substantial improvements in classification performance, achieving a maximum F1-score of 0.98. These results underscore the effectiveness and robustness of our approach in differentiating normal from atrial ECG signals.