ECG Lead Selection for Disease Diagnostics Using CNN-Transformer

Eectrocardiography (ECG) is crucialfor diagnosing cardiovascular diseases (CVDs), which cause millions of deaths each year. This study addresses the challenge of CVD diagnosis in rural areas, where there is a shortage of skilled healthcare professionals and medical equipment. This study proposes a novel method to systematically compare different ECG leads using Deep Learning techniques, specifically a 1D CNN Transformer, to detect anomalies from minimal disturbances. The analysis was conducted using the PTB-XL dataset and further validated with Holter ECG-based records from the St. Petersburg INCART database. Minimal pre-processing was applied, limited to baseline wander removal, to maintain the intrinsic information of each lead. The results indicate that utilizing all leads significantly improves the F1 score, although lead II, V1, and V2 also provide comparable results in the INCART database. This study demonstrates that fewer leads can be effectively used to diagnose diseases, facilitating the creation of low-cost ECG machines suitable for deployment in rural areas. The code is publicly available at https://github.com/nabeelraza-7/ecg-lead-selection.