Atrial Fibrillation and Atrial Flutter detection using Electrocardiomatrix technique from ECG signal

Myocardial infarction, brain attack, thrombus, and heart associated complications result from atrial fibrillation (AFIB) and AFL (Atrial Flutter) are important frequent Heart arrhythmia. This can come to severe health problems in people and in the next stage as a result of many diseases causes by a high mortality rate. The major analytical tool used to interpret and the record is electrocardiogram (ECG) signals. ECG signals are non-linear and difficult to analyze. We proposed a new Electrocardiomatrix (ECM) technology approach for the detection of AFIB and AFL, which is precise and less complex methods, and identifying such conditions that lead to reduces mortality rate. This article's main purpose is to conduct an effective diagnosis of AFIB and AFL using ECM technology. We have evaluated the standard parameters an Accuracy, Sensitivity, Specificity, Precision, and F1-Score. The ECM technique is the processing of atrial activity through an ECG signal. In this process, two-dimensional signals are converted into a three-dimensional colored matrix for rapid discernment of AFIB and AFL. The ECG signals from the Massachusetts Institute of Technology-Beath Israel Hospital's (MIT-BIH) Worldwide Database evaluate methods for Atrial fibrillation (from Physionet.org). The proposed method results are compared between ECM techniques and manual annotations of ECG signals. Therefore, the ECM Techniques achieved Accuracy, Sensitivity, Specificity, Precision, and F1-score are 99.3%, 99.6%, 98.1%, 99%, 97.2% for AFIB and AFL is 99.1%, 99.2%, 98%, 98.4%, 97.6% respectively. AFIB and AFL can be reliably detected using the ECM Technology compared to approaches previously used. This study reveals that the ECM technique effectively analyzed long-term ECG signals by both practitioners and machines without losing accuracy. The proposed method satisfies the demands for high precision, soundness and capacity to evaluate. It is necessary for both healthcare and society to detecting AFIB and AFL, improving patient treatment plans,and better diagnosis.