K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial
Fibrillation Detection
Abstract
Atrial Fibrillation (AF) is an abnormal heart rhythm
which can trigger cardiac arrest and sudden death.
Nevertheless, its interpretation is mostly done by
medical experts due to high error rates of computerized interpretation. One study found that only
about 66% of AF were correctly recognized from
noisy ECGs. This is in part due to insufficient
training data, class skewness, as well as semantical ambiguities caused by noisy segments in an
ECG record. In this paper, we propose a K-marginbased Residual-Convolution-Recurrent neural network (K-margin-based RCR-net) for AF detection
from noisy ECGs. In detail, a skewness-driven dynamic augmentation method is employed to handle
the problems of data inadequacy and class imbalance. A novel RCR-net is proposed to automatically extract both long-term rhythm-level and local
heartbeat-level characters. Finally, we present a Kmargin-based diagnosis model to automatically focus on the most important parts of an ECG record
and handle noise by naturally exploiting expected
consistency among the segments associated for
each record. The experimental results demonstrate
that the proposed method with 0.8125 F1NAOP
score outperforms all state-of-the-art deep learning
methods for AF detection task by 6.8%.