The differential method of phase space matrix for AF/VF discrimination application

The advances in electrocardiographic (ECG) technology have facilitated the development of numerous successful clinical applications and commercial monitoring products for diagnosing disease and monitoring health. All of these demand the development of smart algorithms and computational resources for the real-time, early indication of critical cardiac conditions. This study presents the development of a Complex Phase Space Difference (CPSD) algorithm with differential method to analyze spatial and temporal changes in reconstructed phase space matrix, and derives an index for real-time monitoring. We used total of 5306 data segments from MIT-BIH, CU, and SCDH databases and clinical trial data to determine the optimal working parameters and verified the classification capability by using a quantitative index of this algorithm. With threshold values set to 2.0 and 6.0, this method can successfully differentiate normal sinus rhythm (NSR) signals (1.48 ± 0.21), low risk of atrial fibrillation (AF) signals (3.71 ± 0.99) and high risk of ventricular fibrillation (VF) signals (9.38 ± 2.22). It is the first real-time algorithm that reports the best performance to distinguish AF and VF with sensitivity of 97.9% and specificity of 98.4%. With self-normalization, the algorithm is not subjected to the inter-variability or sampling size effects. Its computational scheme only requires matrices addition and subtraction, and thus significantly reduces the complexity for real-time implementation. It will be able to adopt in different scenarios of tele-healthcare and implantable applications.