  This paper presents an end-to-end ECG signal classification method based on a
novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid
the classification of ECG signals. The ECG segmentation strategy named R-R-R
strategy (i.e., retaining ECG data between the R peaks just before and after
the current R peak) for segmenting the original ECG data into segments in order
to train and test the 1D CNN models. The novel strategy mimics physicians in
scanning ECG to a greater extent, and maximizes the inherent information of ECG
segments. The performance of the classification models for 5-class and 6-class
are verified with ECG signals from 48 records of the MIT-BIH arrhythmia
database. As the heartbeat types are divided into 5 classes (i.e., normal beat,
left bundle branch block beat, right bundle branch block beat, ventricular
ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy,
the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%,
0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6
classes (i.e., normal beat, left bundle branch block beat, right bundle branch
block beat, ventricular ectopic beat, paced beat and other beats) in the
MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the
F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile,
according to the recommended practice from the Association for Advancement of
Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes
(i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats,
fusion beats, and unclassifiable beats), the beat classification accuracy, the
sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The
experimental results show that the proposed method achieves better performance
than the state-of-the-art methods.
