A joint scientific research team has successfully developed an advanced technique for accurately analyzing and classifying electrocardiogram (ECG) signals using artificial intelligence and feature selection algorithms. This innovation contributes to the early detection of heart disorders and enhances smart health monitoring systems.

This technology has been published in the International Journal of Intelligent Engineering and Systems, issued by the Intelligent Networks and Systems Society. The journal is a reputable scientific publication ranked Q2 in Scopus, with a Cite Score of 3.2.

The research team includes Assistant Lecturer Mustafa Noaman from the University of Al-Qadisiyah, College of Computer Science and Information Technology, Assistant Professor Dr. Ali Hamza from Imam Al-Kadhim College, Dr. Safa from Al-Nahrain University, and Dr. Walid from Al-Tusi University.

The researchers demonstrated that ECG signal classification can be significantly improved using the Particle Swarm Optimization (PSO) algorithm for feature selection. Their approach achieved an impressive classification accuracy of 98% using PSO-SVM, compared to 84% without PSO.