ATTENTION BASED JOINT LEARNING FOR SUPER-VISED ELECTROCARDIOGRAM ARRHYTHMIA DIFFER-ENTIATION WITH UNSUPERVISED ABNORMAL BEAT SEGMENTATION

Abstract

Deep learning has shown great promise in arrhythmia classification in electrocardiogram (ECG). Existing works, when classifying an ECG segment with multiple beats, do not identify the locations of the anomalies, which reduces clinical interpretability. On the other hand, segmenting abnormal beats by deep learning usually requires annotation for a large number of regular and irregular beats, which can be laborious, sometimes even challenging, with strong inter-observer variability between experts. In this work, we propose a method capable of not only differentiating arrhythmia but also segmenting the associated abnormal beats in the ECG segment. The only annotation used in the training is the type of abnormal beats and no segmentation labels are needed. Imitating human's perception of an ECG signal, the framework consists of a segmenter and classifier. The segmenter outputs an attention map, which aims to highlight the abnormal sections in the ECG by element-wise modulation. Afterwards, the signals are sent to a classifier for arrhythmia differentiation. Though the training data is only labeled to supervise the classifier, the segmenter and the classifier are trained in an end-to-end manner so that optimizing classification performance also adjusts how the abnormal beats are segmented. Validation of our method is conducted on two dataset. We observe that involving the unsupervised segmentation in fact boosts the classification performance. Meanwhile, a grade study performed by experts suggests that the segmenter also achieves satisfactory quality in identifying abnormal beats, which significantly enhances the interpretability of the classification results.

1. INTRODUCTION

Arrhythmia in electrocardiogram (ECG) is a reflection of heart conduction abnormality and occurs randomly among normal beats. Deep learning based methods have demonstrated strong power in classifying different types of arrhythmia. There are plenty of works on classifying a single beat, involving convolutional neural networks (CNN) (Acharya et al., 2017b; Zubair et al., 2016) , long short-term memory (LSTM) (Yildirim, 2018), and generative adversarial networks (GAN) (Golany & Radinsky, 2019) . For these methods to work in clinical setting, however, a good segmenter is needed to accurately extract a single beat from an ECG segment, which may be hard when abnormal beats are present. Alternatively, other works (Acharya et al., 2017a; Hannun et al., 2019) try to directly identify the genres of arrhythmia present in an ECG segment. The limitation of these works is that they work as a black-box and fail to provide cardiologists with any clue on how the prediction is made such as the location of the associated abnormal beats. In terms of ECG segmentation, there are different tasks such as segmenting ECG records into beats or into P wave, QRS complexity, and T wave. On one hand, some existing works take advantage of signal processing techniques to locate some fiducial points of PQRST complex so that the ECG signals can be divided. For example, Pan-Tompkins algorithm (Pan & Tompkins, 1985) uses a combination of filters, squaring, and moving window integration to detect QRS complexity. The shortcomings of these methods are that handcraft selection of filter parameters and threshold is

