  Supervised deep learning has been widely used in the studies of automatic ECG
classification, which largely benefits from sufficient annotation of large
datasets. However, most of the existing large ECG datasets are roughly
annotated, so the classification model trained on them can only detect the
existence of abnormalities in a whole recording, but cannot determine their
exact occurrence time. In addition, it may take huge time and economic cost to
construct a fine-annotated ECG dataset. Therefore, this study proposes weakly
supervised deep learning models for detecting abnormal ECG events and their
occurrence time. The available supervision information for the models is
limited to the event types in an ECG record, excluding the specific occurring
time of each event. By leverage of feature locality of deep convolution neural
network, the models first make predictions based on the local features, and
then aggregate the local predictions to infer the existence of each event
during the whole record. Through training, the local predictions are expected
to reflect the specific occurring time of each event. To test their potentials,
we apply the models for detecting cardiac rhythmic and morphological
arrhythmias by using the AFDB and MITDB datasets, respectively. The results
show that the models achieve beat-level accuracies of 99.09% in detecting
atrial fibrillation, and 99.13% in detecting morphological arrhythmias, which
are comparable to that of fully supervised learning models, demonstrating their
effectiveness. The local prediction maps revealed by this method are also
helpful to analyze and diagnose the decision logic of record-level
classification models.
