Six Leads Are All You Need for Efficient Cardiac Analysis.

Electrocardiography (ECG) serves as a noninvasive method of monitoring the electrical activity of the heart by employing electrodes attached to the skin. These electrodes detect the minuscule electrical signals generated by the heart’s activity. A conventional ECG, known as a 12-lead ECG, necessitates the positioning of 12 electrodes across the body to yield a detailed perspective of the heart’s electrical function. Our study introduces an innovative approach to real-time ECG signal classification, using a maximum of six leads, with the aim of reducing the time and resources dedicated to data acquisition and signal processing without compromising accuracy. We introduce a technique for grouping leads and assess the performance of the classification of ECG signals on various leads. Our findings indicate that implementing lead-wise grouping with up to six leads results in a 93.67% reduction in sampling time, a 50% decrease in data size on the acquisition device, and an 84.72% cut in signal processing time, all while experiencing a minimal accuracy decrease of 0.08%. The efficacy of the proposed lead grouping has been assessed through the Kafka real-time platform.