A novel framework for electrocardiogram beats recognition based on Hjorth parameters and inferential statistics

This paper presents a novel statistical framework for detecting abnormalities in cardiac beats using Hjorth parameters—Activity, Mobility, and Complexity—derived from ECG signals. The proposed approach leverages the Kolmogorov-Smirnov (KS) 2-Sample Test to quantify the differences between normal and abnormal heartbeats across multiple ECG leads. Unlike traditional methods, our framework focuses on the statistical properties of Hjorth parameters to enhance the accuracy of arrhythmia detection. The framework utilises a multi-lead analysis to accurately differentiate among various beat types, such as regular, APB, PVC, and timed beats, demonstrating a high level of sensitivity. The results demonstrate that specific Hjorth parameters, particularly Activity in Lead II, are highly effective in differentiating between normal and abnormal beats, achieving KS scores as high as 0.99. Additionally, the framework reveals the importance of multi-lead ECG analysis in improving the reliability of beat classification. This study not only introduces a cost-effective and robust method for arrhythmia detection but also lays the groundwork for future research aimed at developing more accurate diagnostic tools based on the statistical analysis of ECG signals.