Advanced QT interval analysis in long-term electrocardiography using shape-based clustering and template matching: A novel approach for Holter monitoring.

ABSTRACT:

Introduction

The QT interval, a critical parameter in electrocardiography (ECG), is challenging to analyze accurately because of variations in T-wave morphology, especially in long-term ECG recordings, such as Holter monitoring. Current methods, which are often manual or semi-automated, lack consistency and efficiency, underscoring the need for a more reliable and automated approach.

Methods

We developed a novel QT interval analysis algorithm that integrates K-shape clustering with Dynamic Time Warping under Limited Warping Path Length for template matching. This method automatically categorizes ECG waveforms into clusters based on shape similarity, and then selects representative template waveforms for each cluster. This approach aimed to standardize and automate the proces

SUBMITTER: Hatano K 

PROVIDER: S-EPMC11872472 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature