4.7 Article

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

FM-ECG: A fine-grained multi-label framework for ECG image classification

Nan Du et al.

Summary: The FM-ECG framework addresses the challenges of detecting abnormalities in real-world clinical ECG data by directly detecting abnormalities on ECG images with weakly supervised fine-grained classification and considering ECG label dependencies, outperforming other state-of-the-art methods.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time

Girmaw Abebe Tadesse et al.

Summary: An end-to-end deep learning approach, DeepMI, was proposed to classify MI from Normal cases and identify the time-occurrence of MI using fusion strategies on 12 ECG leads. Transfer learning was used to reduce computational overhead, and recurrent neural networks were employed to encode longitudinal information in ECGs. DeepMI was validated on a dataset from 17,381 patients and achieved high AUROCs for classifying Normal cases and different onset cases of MI.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Computer Science, Artificial Intelligence

Real-time frequency-independent single-Lead and single-beat myocardial infarction detection

Harold Martin et al.

Summary: This study introduces a novel real-time myocardial infarction detector based on a Deep-LSTM network, achieving stable and high accuracy performance metrics when tested on various datasets. The proposed algorithm could have significant societal and clinical impact by potentially being deployed on existing wearable and portable devices for patients at risk of myocardial infarction.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Computer Science, Interdisciplinary Applications

MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning

Ziyang He et al.

Summary: The paper introduces a lightweight MI diagnosis system based on a multi-feature-branch lead attention neural network utilizing 12 leads ECG signals. Experimental results show high accuracy in different databases and the system is capable of real-time diagnosis.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Computer Science, Artificial Intelligence

A Survey on Evolutionary Construction of Deep Neural Networks

Xun Zhou et al.

Summary: Automated construction of deep neural networks is a challenging research topic due to the difficulty of finding the most suitable architecture and parameters for a given task. This study formulates the process as a multilevel multiobjective optimization problem and explores the use of evolutionary algorithms for solving it. By reviewing existing techniques, the study aims to provide insights for researchers to effectively utilize EAs in automated DNN construction.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2021)

Article Computer Science, Information Systems

ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction

Yangjie Cao et al.

Summary: A novel multi-channel lightweight model (ML-Net) is proposed for convenient real-time myocardial infarction (MI) detection, which outperforms comparable schemes in diagnosing MI with lower computational cost and memory usage. This model assigns each ECG lead an independent channel, ensuring data independence and preserving ECG characteristics represented by different leads. Extensive experiments show the effectiveness of ML-Net in diagnosing MI and its potential for wider use in the Internet of Medical Things (IoMT) field.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Engineering, Biomedical

Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using Long Short-Term Memory Neural Network

Harold Martin et al.

Summary: This study introduces a new LSTM neural network architecture for diagnosing myocardial infarctions from single-lead ECGs, trained using unbiased patient split method. The research explores the impact of data-split techniques on overfitting, leakage, and bias, providing a comprehensive assessment of model performance. The achieved real-time diagnosis accuracy and modularity of the LSTM network structure suggest a promising direction for unbiased diagnosis and early treatment planning.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Engineering, Biomedical

Self-supervised ECG pre-training

Han Liu et al.

Summary: The combination of self-supervised learning and unique data augmentation methods effectively alleviated the imbalance and label scarcity issues in ECG data, achieving significant results in ECG morphology recognition.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Proceedings Paper Computer Science, Hardware & Architecture

Energy-Aware Design Methodology for Myocardial Infarction Detection on Low-Power Wearable Devices

Mohanad Odema et al.

Summary: This paper proposes a methodology to automate the design space exploration of neural network architectures for MI detection. By utilizing MOBO for NAS, Pareto optimal architectural models that minimize detection error and energy consumption on the target device are generated. Inspired by BCNNs suited for mobile health applications, the performance of these models is validated and one model achieves a state-of-the-art accuracy of 91.22% on wearable devices.

2021 26TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC) (2021)

Article Computer Science, Interdisciplinary Applications

ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG

Chuang Han et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Cardiac & Cardiovascular Systems

European Society of Cardiology: Cardiovascular Disease Statistics 2019

Adam Timmis et al.

EUROPEAN HEART JOURNAL (2020)

Article Computer Science, Information Systems

MFB-CBRNN: A Hybrid Network for MI Detection Using 12-Lead ECGs

Wenhan Liu et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Article Computer Science, Artificial Intelligence

Evolving Deep Convolutional Neural Networks for Image Classification

Yanan Sun et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2020)

Article Computer Science, Artificial Intelligence

Completely Automated CNN Architecture Design Based on Blocks

Yanan Sun et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Multidisciplinary Sciences

PTB-XL, a large publicly available electrocardiography dataset

Patrick Wagner et al.

SCIENTIFIC DATA (2020)

Article Computer Science, Artificial Intelligence

Squeeze-and-Excitation Networks

Jie Hu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Article Automation & Control Systems

Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification

Yanan Sun et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

Article Computer Science, Interdisciplinary Applications

Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features

Chuang Han et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2019)

Article Engineering, Electrical & Electronic

Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach

Lakhan Dev Sharma et al.

SIGNAL IMAGE AND VIDEO PROCESSING (2018)

Article Computer Science, Information Systems

Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection

Wenhan Liu et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2018)

Article Engineering, Biomedical

Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram

Wenhan Liu et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2018)

Article Computer Science, Information Systems

Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

U. Rajendra Acharya et al.

INFORMATION SCIENCES (2017)

Article Computer Science, Information Systems

Deep Learning for Health Informatics

Daniele Ravi et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2017)

Article Engineering, Biomedical

Third-order tensor based analysis of multilead ECG for classification of myocardial infarction

Sibasankar Padhy et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2017)

Article Computer Science, Interdisciplinary Applications

ECG-based heartbeat classification for arrhythmia detection: A survey

Eduardo Jose da S. Luz et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)

Article Engineering, Biomedical

Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

L. N. Sharma et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2015)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Engineering, Biomedical

Automated analysis of ECG waveforms with atypical QRS complex morphologies

Reza Tafreshi et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2014)

Article Cardiac & Cardiovascular Systems

The forgotten lead: Does aVR ST-deviation add insight into the outcomes of ST-elevation myocardial infarction patients?

Aws Alherbish et al.

AMERICAN HEART JOURNAL (2013)

Article Engineering, Biomedical

ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform

Roshan Joy Martis et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2013)

Article Computer Science, Artificial Intelligence

Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models

Pei-Chann Chang et al.

APPLIED SOFT COMPUTING (2012)

Article Engineering, Biomedical

ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection

Li Sun et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2012)

Article Cardiac & Cardiovascular Systems

Third Universal Definition of Myocardial Infarction

Kristian Thygesen et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2012)

Article Engineering, Biomedical

Identification of myocardial infarction (MI) using spatio-temporal heart dynamics

Hui Yang et al.

MEDICAL ENGINEERING & PHYSICS (2012)

Article Health Care Sciences & Services

Analysis of Myocardial Infarction Using Discrete Wavelet Transform

E. S. Jayachandran et al.

JOURNAL OF MEDICAL SYSTEMS (2010)

Review Medicine, General & Internal

Current concepts - Use of the electrocardiogram in acute myocardial infarction

PJ Zimetbaum et al.

NEW ENGLAND JOURNAL OF MEDICINE (2003)