Arrhythmia Classification

Arrhythmia classification aims to automatically identify different heart rhythm abnormalities from electrocardiograms (ECGs) or other physiological signals, improving diagnostic accuracy and enabling timely interventions. Current research focuses on developing efficient and accurate classification models, employing deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph convolutional networks (GCNs), often coupled with signal processing techniques like visibility graphs or short-time Fourier transforms. These advancements are driven by the need for low-power, resource-efficient algorithms suitable for deployment on wearable devices for continuous monitoring, ultimately improving patient care and reducing healthcare burdens.

Papers

September 10, 2024

Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
Dong Han, Jihye Moon, Luís Roberto Mercado Díaz, Darren Chen, Devan Williams, Eric Y. Ding, Khanh-Van Tran, David D. McManus, Ki H. Chon
PPG Signal Health Monitoring Photoplethysmography Signal Arrhythmia Classification

April 19, 2024

Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks
Rafael F. Oliveira, Gladston J. P. Moreira, Vander L. S. Freitas, Eduardo J. S. Luz
ECG Signal Arrhythmia Detection Visibility Graph Arrhythmia Classification

February 16, 2024

A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers
Paola Busia, Matteo Antonio Scrugli, Victor Jean-Baptiste Jung, Luca Benini, Paolo Meloni
32 Bit Microcontrollers Tiny Transformer MIT BIH Arrhythmia Wearable Data Collection System Arrhythmia Classification Wearable System

October 24, 2023

EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
Benyamin Haghi, Lin Ma, Sahin Lale, Anima Anandkumar, Azita Emami
Deep Learning Analog Neural Network Analog Computing Arrhythmia Classification Virtual Analog

August 3, 2023

Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification
Yun Kwan Kim, Minji Lee, Kunwook Jo, Hee Seok Song, Seong-Whan Lee
Attention Mechanism Atrial Fibrillation Clinical Decision Support System Arrhythmia Detection MIT BIH Arrhythmia Arrhythmia Classification

April 4, 2023

Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network
Ninghao Pu, Zhongxing Wu, Ao Wang, Hanshi Sun, Zijin Liu, Hao Liu
Binary Neural Network Electrocardiogram Classification Lightweight Deep Arrhythmia Classification Binary Neural

January 23, 2023

ECGAN: Self-supervised generative adversarial network for electrocardiography
Lorenzo Simone, Davide Bacciu
Generative Adversarial Network Self Supervised Synthetic Data State of the Art Generative Synthetic Electrocardiogram ECG Datasets Arrhythmia Classification

June 28, 2022

ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique
Minh Cao, Tianqi Zhao, Yanxun Li, Wenhao Zhang, Peyman Benharash, Ramin Ramezani
Deep Learning Convolutional Neural Network Short Time Fourier Transform Electrocardiogram Classification Deep Transfer MIT BIH Arrhythmia Arrhythmia Classification

May 7, 2022

Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices
Ao Wang, Wenxing Xu, Hanshi Sun, Ninghao Pu, Zijin Liu, Hao Liu
Neural Network Deep Learning Resource Constrained Device ECG Analysis MIT BIH Arrhythmia Wearable Single Lead Electrocardiogram Arrhythmia Classification

February 27, 2022

Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization
Hanshi Sun, Ao Wang, Ninghao Pu, Zhiqing Li, Junguang Huang, Hao Liu, Zhi Qi
Deep Learning Convolutional Neural Network Wearable Device Adaptive Loss Arrhythmia Detection Network Quantization Arrhythmia Classification

Arrhythmia Classification

Papers

Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings

Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks

A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers

EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification

Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification

Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network

ECGAN: Self-supervised generative adversarial network for electrocardiography

ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique

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

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