QRS Detection in Electrocardiogram Based on Smart Algorithm

The accurate detection of QRS complexes in electrocardiograms (ECGs) is crucial for diagnosing various cardiac conditions. The QRS complex is the combination of three of the graphical deflections seen on a typical electrocardiogram (ECG or EKG). This project divided two parts, the first part was done by the simulation which presents the detection process of QRS using classical method named Pan and Tompkins algorithm (PTA). The data for this study was obtained from the PhysioNet heart database, which is a well-known resource for analyzing cardiovascular signals. The proposed method leverages advanced signal processing techniques to identify QRS complexes with high precision. The algorithm's performance is evaluated using standard ECG datasets, demonstrating significant improvements in detection accuracy compared to traditional methods. The technique that was deployed demonstrated a remarkable level of precision, attaining a 99.3% accuracy rate through the utilization of neural network for classification. Additionally, the smart algorithm's adaptability to different ECG signal variations and noise levels underscores its potential for real-world applications in smart devices and remote cardiac monitoring systems. This advancement in QRS detection technology represents a significant step forward in the development of intelligent healthcare solutions, enabling more effective and timely cardiac care.