  Cardiovascular disease (CVDs) is one of the universal deadly diseases, and
the detection of it in the early stage is a challenging task to tackle.
Recently, deep learning and convolutional neural networks have been employed
widely for the classification of objects. Moreover, it is promising that lots
of networks can be deployed on wearable devices. An increasing number of
methods can be used to realize ECG signal classification for the sake of
arrhythmia detection. However, the existing neural networks proposed for
arrhythmia detection are not hardware-friendly enough due to a remarkable
quantity of parameters resulting in memory and power consumption.
  In this paper, we present a 1-D adaptive loss-aware quantization, achieving a
high compression rate that reduces memory consumption by 23.36 times. In order
to adapt to our compression method, we need a smaller and simpler network. We
propose a 17 layer end-to-end neural network classifier to classify 17
different rhythm classes trained on the MIT-BIH dataset, realizing a
classification accuracy of 93.5%, which is higher than most existing methods.
Due to the adaptive bitwidth method making important layers get more attention
and offered a chance to prune useless parameters, the proposed quantization
method avoids accuracy degradation. It even improves the accuracy rate, which
is 95.84%, 2.34% higher than before. Our study achieves a 1-D convolutional
neural network with high performance and low resources consumption, which is
hardware-friendly and illustrates the possibility of deployment on wearable
devices to realize a real-time arrhythmia diagnosis.
