RMS Shifting Based Approach for R – Peak Identification and Real-Time Cardiac Arrhythmia Detection

The detection of R-peaks in electrocardiogram (ECG) signals are vital for precise heart rate calculation and in the detection of cardiac arrhythmias, which can lead to severe health complications if not recognized in a timely manner. Arrhythmias, marked by abnormal heart rhythms, can considerably elevate the risk of stroke, heart failure, and sudden cardiac arrest. Therefore, accurate and dependable detection of these irregularities is essential for prompt medical intervention and effective patient care. Conventional QRS detection algorithms frequently encounter difficulties due to noise, signal irregularities, and baseline drift, which can result in false negatives during peak detection and unreliable heart rate estimations. Furthermore, these approaches often demand considerable memory resources to monitor undetected peaks, complicating real-time analysis. As a result, these limitations can hinder effective patient monitoring and timely intervention in critical care situations. In this study, we come up with a new R – peak identification algorithm that employs the root mean square (RMS) shifting technique, aimed at improving real-time detection of cardiac arrhythmias. The RMS shifting approach modifies the ECG signal by subtracting a value equivalent to the Root Mean Square of the pre-processed data, which effectively shifts P and T waves to the lower y-range while maintaining the R peaks to the upper range. This approach allows for the soft detection of R peaks making it robust against noise and signal distortion. Implemented in LabVIEW, our model has been assessed on Physionet ECG dataset demonstrating performance metrics comparable to those achieved by the commonly used Fast Fourier Transform (FFT) spectral analysis method.