Two solutions for the processing of ambulatory electrocardiogram

Abstract

The paper addresses two challenges in ambulatory electrocardiogram (ECG) processing: identifying valid heartbeats for heart rate variability (HRV) analysis and robust detection of the T-wave end (Te point) under noisy conditions. Proposed methods include computational intelligence techniques (KPCA + MLP) and a trapezoidal area-based algorithm (TRA). Results show superior accuracy and noise resilience compared to conventional approaches.