MIT BIH Arrhythmia

The MIT-BIH Arrhythmia Database is a widely used benchmark for developing and evaluating algorithms that automatically detect and classify cardiac arrhythmias from electrocardiogram (ECG) signals. Current research focuses on improving the accuracy and efficiency of these algorithms, employing various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), transformers, and generative adversarial networks (GANs), often combined with techniques like feature fusion and transfer learning. This work is crucial for advancing the development of accurate, resource-efficient, and interpretable diagnostic tools for real-time, continuous cardiac monitoring, potentially leading to improved early detection and management of cardiovascular diseases.

Papers

October 9, 2024

rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG
Shreya Srivastava, Durgesh Kumar, Jatin Bedi, Sandeep Seth, Deepak Sharma
Demographic Attribute Single Lead Electrocardiogram Data ECG Classification ECG Analysis Arrhythmia Detection MIT BIH Arrhythmia Multimodal Architecture

May 24, 2024

Resource-Efficient Heartbeat Classification Using Multi-Feature Fusion and Bidirectional LSTM
Reza Nikandish, Jiayu He, Benyamin Haghi
Feature Fusion Bi LSTM Electrocardiogram Classification MIT BIH Arrhythmia Bidirectional LSTM ECG Monitoring System

April 8, 2024

EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection
JuneYoung Park, Da Young Kim, Yunsoo Kim, Jisu Yoo, Tae Joon Kim
Anomaly Detection Abnormal Signal Electrocardiogram Data MIT BIH Arrhythmia

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

November 21, 2023

Harnessing FPGA Technology for Enhanced Biomedical Computation
Nisanur Alici, Kayode Inadagbo, Murat Isik
Long Short Term Memory Field Programmable Gate Array MIT BIH Arrhythmia FPGA Implementation

November 20, 2023

A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular Diseases Detection
Siyang Wu
Long Short Term Memory Support Vector Machine Electrocardiogram Signal Atrial Fibrillation MIT BIH Arrhythmia Cardiovascular Disease Detection

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 MIT BIH Arrhythmia Arrhythmia Detection Arrhythmia Classification

May 26, 2023

A Novel real-time arrhythmia detection model using YOLOv8
Guang Jun Nicholas Ang, Aritejh Kr Goil, Henryk Chan, Jieyi Jeric Lew, Xin Chun Lee, Raihan Bin Ahmad Mustaffa, Timotius Jason, Ze Ting Woon, Bingquan Shen
Detection Model Look Once Version 8 Arrhythmia Detection MIT BIH Arrhythmia

March 7, 2023

ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks
Aryan Odugoudar, Jaskaran Singh Walia
Deep Learning Convolutional Neural Network ECG Signal Lead ECG Arrhythmia Detection MIT BIH Arrhythmia Cardiac Arrhythmia

October 25, 2022

Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Bartosz Grabowski, Przemysław Głomb, Wojciech Masarczyk, Paweł Pławiak, Özal Yıldırım, U Rajendra Acharya, Ru-San Tan
Machine Learning Convolutional Neural Network Self Supervised Classification Code Electrocardiogram Signal Electrocardiogram Analysis MIT BIH Arrhythmia

October 19, 2022

Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals
Zekai Wang, Stavros Stavrakis, Bing Yao
Generative Adversarial Network Electrocardiogram Signal Electrocardiogram Data MIT BIH Arrhythmia Hierarchical Deep Learning Heart Disease Detection

October 5, 2022

Two-stream Network for ECG Signal Classification
Xinyao Hou, Shengmei Qin, Jianbo Su
Electrocardiogram Signal Electrocardiogram Data Electrocardiogram Classification ECG Classification Stream Network MIT BIH Arrhythmia

August 29, 2022

A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection
Negin Alamatsaz, Leyla s Tabatabaei, Mohammadreza Yazdchi, Hamidreza Payan, Nima Alamatsaz, Fahimeh Nasimi
Long Short Term Memory MIT BIH Arrhythmia Arrhythmia Detection Electrocardiogram Arrhythmia

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

April 7, 2022

Global ECG Classification by Self-Operational Neural Networks with Feature Injection
Muhammad Uzair Zahid, Serkan Kiranyaz, Moncef Gabbouj
Electrocardiogram Classification ECG Classification MIT BIH Arrhythmia Arrhythmia Detection Feature Splatting

January 26, 2022

Arrhythmia Classification using CGAN-augmented ECG Signals
Edmond Adib, Fatemeh Afghah, John J. Prevost
Electrocardiogram Representation Auxiliary Classifier Synthetic Electrocardiogram MIT BIH Arrhythmia Electrocardiogram Generation

January 10, 2022

Improving ECG Classification Interpretability using Saliency Maps
Yola Jones, Fani Deligianni, Jeff Dalton
Saliency Map Electrocardiogram Signal Electrocardiogram Classification Electrocardiogram Recording Electrocardiogram Feature MIT BIH Arrhythmia

December 15, 2021

Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things
Berken Utku Demirel, Islam Abdelsalam Bayoumy, Mohammad Abdullah Al Faruque
Wearable Device Heart Rate ECG Signal Beat Tracking MIT BIH Arrhythmia Low Power Wearable

MIT BIH Arrhythmia

Papers

rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG

Resource-Efficient Heartbeat Classification Using Multi-Feature Fusion and Bidirectional LSTM

EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection

A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers

Harnessing FPGA Technology for Enhanced Biomedical Computation

A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular Diseases Detection

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

A Novel real-time arrhythmia detection model using YOLOv8

ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks

Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals

Two-stream Network for ECG Signal Classification

A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection

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

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

Arrhythmia Classification using CGAN-augmented ECG Signals

Improving ECG Classification Interpretability using Saliency Maps

Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things