Aim. ECG signals are distorted during Magnetic Reso- nance Imaging (MRI) by the electromagnetic environment. Automated analysis of ECG is therefore highly difficult. The detection of pathological heartbeats is currently lim- ited to excluding heartbeats with outstanding RR intervals. This study aimed at implementing a machine-learning (ML) based heartbeat classifier, using hand-crafted fea- tures, for the automatic detection of ventricular heartbeats during MRI examinations.
Method. A model was trained and assessed on the MIT- BIH Arrhythmia Database, using the AAMI recommenda- tions for class labelling and division in two equal subsets DS1 and DS2. A single lead of ECG signals was analyzed. Two classes of features were extracted for each heartbeat: (i) QRS morphological features based on Hermite func- tion decomposition, (ii) temporal features based on the lo- cal RR interval time-series around the heartbeat. A sup- port vector machine was trained to detect normal (N), and ventricular ectopic beats (V') using these features on DS1. The model was then tested on an in-house database of ECG acquired inside a 1.5T MRI scanner during standard clin- ical examination.
Results The classifier achieved F1 scores of 0.99, 0.85 on N and V' classes respectively on DS2. While the classi- fier only achieved F1 scores of 0.62 and 0.15 on the ECG signals acquired in MRI.
Conclusion A heartbeat classifier was developed on the MIT-BIH arrhythmia database focusing on temporal fea- tures and morphological features focsing mainly on the QRS complex (as they are less likely to be distorted by the MRI environment). Performance on MIT-BIH was ac- ceptable although slightly lower than state-of-the-art ap- proaches, but dropped significantly on MRI data. The re- sults highlight the need for further developments by adding MRI-related artifact reduction, while also retraining the classifier on MRI acquired datasets.