Automated Detection of Ventricular Heartbeats from Electrocardiogram (ECG) Acquired During Magnetic Resonance Imaging (MRI)

Pierre G Aublin, J. Felblinger, J. Oster
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Abstract

ECGs are highly distorted by the MRI environment, making automated ECG analysis highly difficult. This study aimed at implementing a machine-learning (ML) based heartbeat classifier, using hand-crafted features, for the automatic detection of ventricular heartbeats during MRI. A model was trained on the MIT-BIH Arrhythmia Database and assessed on an in-house database of ECG acquired inside a 1.5T MRI (ECG-MRI). Features were extracted for each heartbeat from single-lead ECG signals including QRS morphological features based on Hermite functions, and RR interval-based features. A support vector machine was trained to classify normal (N) and ventricular ectopic beats (V‘). The classifier achieved F1 scores of 0.85 on the V' class on the validation fold on the MIT-BIH database, while it only achieved F1 scores of 0.15 on the ECG-MRI database. The proposed heartbeat classifier was developed on the MIT-BIH arrhythmia database using temporal features and QRS morphological features based on the assumption they would be less distorted by the MRI environment. However, even if performance on MIT-BIH were acceptable (although slightly lower than state-of-the-art approaches), results were poor on the ECG-MRI database. The results highlight the need for further developments by suppressing MRI-related artifacts, and by retraining on MRI specific datasets.
从磁共振成像(MRI)获得的心电图(ECG)中自动检测心室心跳
心电图受到MRI环境的高度扭曲,使得自动心电图分析非常困难。本研究旨在实现基于机器学习(ML)的心跳分类器,使用手工制作的特征,用于MRI期间心室心跳的自动检测。在MIT-BIH心律失常数据库上训练模型,并在1.5T MRI (ECG-MRI)内获得的内部心电图数据库上评估模型。从单导联心电信号中提取每次心跳的特征,包括基于Hermite函数的QRS形态学特征和基于RR间隔的特征。训练支持向量机分类正常(N)和室性异位搏(V’)。该分类器在MIT-BIH数据库上验证折叠的V'类上获得了0.85的F1分数,而在ECG-MRI数据库上仅获得了0.15的F1分数。所提出的心跳分类器是在MIT-BIH心律失常数据库上开发的,基于时间特征和QRS形态学特征,假设它们不会被MRI环境扭曲。然而,即使在MIT-BIH上的表现是可以接受的(尽管比最先进的方法略低),在ECG-MRI数据库上的结果也很差。研究结果强调了通过抑制MRI相关伪影和对MRI特定数据集进行再训练来进一步发展的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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