Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization

02/27/2022
by   Hanshi Sun, et al.
0

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 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 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.

READ FULL TEXT

page 1

page 5

research
05/07/2022

Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices

Monitoring electrocardiogram signals is of great significance for the di...
research
06/08/2022

Binary Single-dimensional Convolutional Neural Network for Seizure Prediction

Nowadays, several deep learning methods are proposed to tackle the chall...
research
07/10/2016

Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network

Deep convolutional neural network (DCNN) has achieved remarkable perform...
research
02/06/2022

Energy awareness in low precision neural networks

Power consumption is a major obstacle in the deployment of deep neural n...
research
03/23/2018

SqueezeNext: Hardware-Aware Neural Network Design

One of the main barriers for deploying neural networks on embedded syste...
research
01/19/2023

Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size

Tsetlin machine (TM) is a logic-based machine learning approach with the...
research
09/03/2022

SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG

This paper proposes SaleNet - an end-to-end convolutional neural network...

Please sign up or login with your details

Forgot password? Click here to reset