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

@article{Wang2022ArrhythmiaCU,
  title={Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices},
  author={Ao Wang and Wenxing Xu and Hanshi Sun and Ninghao Pu and Zijing Liu and Hao Liu},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.03661}
}
—Monitoring electrocardiogram signals is of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not friendlyF to resource-constrained equipment, and it is difficult to realize real-time monitoring. In this paper, a binarized convolutional… 
[PDF] Semantic Reader

References

SHOWING 1-10 OF 32 REFERENCES
Arrhythmia Classifier Using a Layer-wise Quantized Convolutional Neural Network for Resource-Constrained Devices
TLDR
A novel neural network classifier is proposed to classify 17 different rhythm classes using 1,000 long-duration electrocardiograms, achieving a classification accuracy of 95.72%, which is 4.32% higher than current state-of-the-art methods.
An Efficient Arrhythmia Classifier Using Convolutional Neural Network with Incremental Quantification
TLDR
This paper presents an efficient arrhythmia classifier based on the convolutional neural network with the incremental quantification, which is beneficial to hardware design and has better accuracy than other advanced quantification methods.
Electrocardiogram soft computing using hybrid deep learning CNN-ELM
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
TLDR
A fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system that achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopy beats.
Patient-specific ECG classification by deeper CNN from generic to dedicated
Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings
TLDR
The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices and demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering.
Resource and Energy Efficient Implementation of ECG Classifier Using Binarized CNN for Edge AI Devices
TLDR
This paper explores efficient implementation of binary convolutional neural network employing function merging and block reuse techniques to classify ventricular beat in electrocardiogram using hardware implemented in field programmable gate array (FPGA) platform.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
TLDR
It is demonstrated that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists.
Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria
TLDR
This classifier model has fewer features and performs better than other state-of-the-art methods with results suggesting better generalization capability.
Arrhythmia detection and classification using morphological and dynamic features of ECG signals
TLDR
A new approach for arrhythmia classification based on a combination of morphological and dynamic features is proposed, which yields an overall accuracy of 99.66% on 85945 heartbeats, better than any other published results.
...
...