Three-Dimensional Attractor Reconstruction for Enhanced ECG Classification of Arrhythmia and Congestive Heart Failure

This paper proposes a novel technique that leverages the entire ECG waveform to detect subtle shape changes in long-duration data streams. The approach employs symmetric projection attractor reconstruction to generate a 3-dimensional attractor from time-delayed segments of raw ECG waveforms. By projecting the attractor onto a two-dimensional plane, baseline variations are minimized, and novel quantitative features (radial density and theta density) characterizing the shape and variability of sensor data are extracted. These features are collected over sliding time windows and fused to serve as input for machine learning classifiers. To validate the proposed algorithm, a publicly available dataset comprising 162 ECG signals was utilized, including 96 cases of arrhythmia, 30 cases of congestive heart failure (CHF), and 36 cases of normal sinus rhythm (NSR). The proposed method achieved a validation accuracy of 94% and a test accuracy of 93.2% in classifying the three classes using a weighted K-nearest neighbour (KNN) model. Obtained results underscore the robustness and simplicity of the presented approach in diagnosing cardiac abnormalities, providing a promising pathway for enhanced biomedical sensor-based diagnosis.