MATLAB Implementation for ECG Detection with R-wave Identification

ECG detection is a fundamental medical monitoring technique used to detect and record the electrical activity of the heart. Through automated R-wave identification, ECG detection provides crucial information about cardiac rhythm and heart health status. The R-wave detection functionality enhances the convenience and accuracy of ECG monitoring, enabling physicians to rapidly diagnose and track patients' cardiac conditions. From an implementation perspective, MATLAB-based ECG detection typically involves several key processing stages: signal preprocessing (filtering to remove noise using functions like butterworth filters), QRS complex detection employing algorithms such as Pan-Tompkins or wavelet transforms, and R-wave peak identification using peak detection functions like findpeaks with appropriate threshold settings. The algorithm often includes adaptive threshold mechanisms to handle varying signal amplitudes across different patients. This automated detection capability makes ECG monitoring particularly valuable in clinical medicine, where it's widely applied in cardiac disease diagnosis and treatment management. Whether in hospital settings or home environments, ECG detection helps individuals better understand and manage their cardiovascular health through continuous rhythm analysis and anomaly detection features implemented via MATLAB's signal processing toolbox and biomedical data analysis functions.