Congestive heart failure detection based on Electrocardiomatrix Method with ECG Signal

Congestive heart failure (CHF) is a prevalent, expensive to treat, and dangerous disease inwhich the pumping capacity of the heart muscle is reduced due to injury or stress. It causesmajor medical problems in humans and contribute to many diseases, thus increasing themortality rate. In a world with a growing population, there is a need for more precise andsimpler approaches to detect such conditions, which can prevent many diseases and lead toa lower mortality rate. The main goal here is to use electrocardiomatrix (ECM) approachto perform the task of detecting CHF. It is detected quickly and accurately with thisapproach, as ECM converts 2D electrocardiogram (ECG) data into a 3D-colored matrix.The approach is tested using ECG readings from the Beth Israel Deaconess Medical Center(BIDMC) CHF Database on the Internet (Physionet.org). The ECM outcomes of are thencompared to manual readings of ECG data. The ECM results achieved the accuracy of96.89%, the sensitivity of 97.53%, the precision of 99.1%, the F1-score of 97.76%, and thespecificity of 96.02% for CHF. This research shows that the ECM approach is a good wayfor machines and practitioners to interpret long-term ECG readings while maintainingaccuracy.