Advancing ECG Signal Classification With a Fuzzy Classifier Approach

The analysis of ECG signals plays an important role in healthcare, particularly for the detection of heart conditions such as arrhythmia, coronary artery disease, or heart attack. Accurate diagnosis often depends on the effective classification of ECG signals. Over the years, numerous methods and algorithms have been developed to classify ECG signals with reliable accuracy. These methods generally follow a two-stage process: signal preprocessing and classification. Preprocessing is focused on removing noise and extracting relevant features that are used in the classification stage. However, preprocessing can also lead to the loss of small but potentially important information, which may affect the performance of classification algorithms. In this paper, we present an approach for ECG signal classification that addresses the issue of information loss during preprocessing. The proposed method incorporates a fuzzy classifier in the classification stage, which is designed to handle the uncertainty introduced by the loss of information. The proposed approach is evaluated through experimental studies, which include comparisons with other classification approaches. The results show that the use of a fuzzy classifier can improve the accuracy of ECG signal classification, especially in cases where preprocessing leads to information loss. The findings suggest that fuzzy classifiers are suitable for ECG signal analysis.