An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation

TL;DR

This paper introduces KEED, a deep learning model for ECG signal delineation that directly estimates keypoints, eliminating the need for post-processing and significantly improving efficiency and performance with limited data.

Contribution

KEED is a novel keypoint estimation deep learning model that aligns output with clinical expectations and outperforms state-of-the-art methods without extensive data or post-processing.

Findings

KEED outperforms state-of-the-art methods in ECG delineation.

KEED reduces inference time by up to 703 times.

KEED performs well with limited annotated data.

Abstract

Deep Learning (DL) methods have been used for electrocardiogram (ECG) processing in a wide variety of tasks, demonstrating good performance compared with traditional signal processing algorithms. These methods offer an efficient framework with a limited need for apriori data pre-processing and feature engineering. While several studies use this approach for ECG signal delineation, a significant gap persists between the expected and the actual outcome. Existing methods rely on a sample-to-sample classifier. However, the clinical expected outcome consists of a set of onset, offset, and peak for the different waves that compose each R-R interval. To align the actual with the expected output, it is necessary to incorporate post-processing algorithms. This counteracts two of the main advantages of DL models, since these algorithms are based on assumptions and slow down the method's performance. In this paper, we present Keypoint Estimation for Electrocardiogram Delineation (KEED), a novel DL model designed for keypoint estimation, which organically offers an output aligned with clinical expectations. By standing apart from the conventional sample-to-sample classifier, we achieve two benefits: (i) Eliminate the need for additional post-processing, and (ii) Establish a flexible framework that allows the adjustment of the threshold value considering the sensitivity-specificity tradeoff regarding the particular clinical requirements. The proposed method's performance is compared with state-of-the-art (SOTA) signal processing methods. Remarkably, KEED significantly outperforms despite being optimized with an extremely limited annotated data. In addition, KEED decreases the inference time by a factor ranging from 52x to 703x.