MSGformer: A multi-scale grid transformer network for 12-lead ECG arrhythmia detection

Abstract

The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to identify arrhythmias that are characterized by anomalous waveform morphology and erratic intervals. Current ECG analysis methods primarily rely on the feature extraction of single leads or scales, thereby overlooking the critical complementary data obtainable from multiple channels and scales. This paper introduces the Multi-Scale Grid Transformer (MSGformer) network, which extracts spatial features from limb and chest leads and employs a multi-scale grid attention mechanism to capture temporal features. The self-attention mechanism-based multi-lead feature fusion approach leverages diverse leads’ perspectives to reflect each lead’s heart’s comprehensive state and extract unique essential features. Furthermore, MSGformer incorporates a multi-scale grid attention feature extraction strategy that employs multi-head and multi-scale attention mechanisms to extract multi-scale temporal features from two dimensions. The MSGformer network combines these feature extraction strategies, resulting in simultaneous capturing of morphological characteristics across different leads and temporal characteristics within the same lead in ECG. This integration facilitates the effective detection of morphological abnormalities and erratic intervals in cardiac electrical activity. Utilizing the publicly available 2018 China Physiological Signal Challenge (CPSC 2018) and MIT-BIH electrocardiogram datasets, the performance of MSGformer was evaluated and compared to existing ECG classification models. Experimental results demonstrate that MSGformer achieved an F1 score of 0.86, while on the MIT-BIH dataset, it attained accuracy, sensitivity, and positive predictive value of 99.28%, 97.13%, and 97.87%, respectively, outperforming other current models.