An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional LSTM network

PlexNet: A Fast and Robust ECG Biometric System for Human Recognition

(2021) Ranjeet Srivastva et al.   INFORMATION SCIENCES

Atrial fibrillation detection with and without atrial activity analysis using lead-I mobile ECG technology

(2021) Gergely Tuboly et al.   Biomedical Signal Processing and Control

Automated detection of shockable ECG signals: A review

(2021) Mohamed Hammad et al.   INFORMATION SCIENCES

A novel method for automated congestive heart failure and coronary artery disease recognition using THC-Net

(2021) Weiyi Yang et al.   INFORMATION SCIENCES

Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems

(2020) Paolo Massimo Buscema et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network

(2020) Dalibor Cimr et al.   INFORMATION SCIENCES

Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks

(2020) Yanrui Jin et al.   KNOWLEDGE-BASED SYSTEMS

Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets

(2020) Georgios Petmezas et al.   Biomedical Signal Processing and Control

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

(2020) Gerald Hirsch et al.   EXPERT SYSTEMS WITH APPLICATIONS

Computer Aided detection for fibrillations and flutters using deep convolutional neural network

(2019) Hamido Fujita et al.   INFORMATION SCIENCES

Detection of atrial fibrillation using discrete-state Markov models and Random Forests

(2019) Vignesh Kalidas et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Attention augmentation with multi-residual in Bidirectional LSTM

(2019) Ye Wang et al.   NEUROCOMPUTING

Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process

(2019) Jibin Wang et al.   Biomedical Signal Processing and Control

Automated arrhythmia classification based on a combination network of CNN and LSTM

(2019) Chen Chen et al.   Biomedical Signal Processing and Control

A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network

(2019) Jibin Wang   Future Generation Computer Systems-The International Journal of eScience

Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network

(2019) Qihang Yao et al.   Information Fusion

Automated detection of atrial fibrillation and atrial flutter in ECG signals based on convolutional and improved Elman neural network

(2019) Jibin Wang   KNOWLEDGE-BASED SYSTEMS

Squeeze-and-Excitation Networks

(2019) Jie Hu et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform

(2018) Mohit Kumar et al.   Biocybernetics and Biomedical Engineering

Detecting atrial fibrillation by deep convolutional neural networks

(2018) Yong Xia et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

(2018) Oliver Faust et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network

(2018) U. Rajendra Acharya et al.   Future Generation Computer Systems-The International Journal of eScience

Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review

(2018) Yuki Hagiwara et al.   INFORMATION SCIENCES

A deep learning approach for real-time detection of atrial fibrillation

(2018) Rasmus S. Andersen et al.   EXPERT SYSTEMS WITH APPLICATIONS

Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network

(2017) U. Rajendra Acharya et al.   INFORMATION SCIENCES

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

(2017) U. Rajendra Acharya et al.   INFORMATION SCIENCES

Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients

(2017) Bahareh Pourbabaee et al.   IEEE Transactions on Systems Man Cybernetics-Systems

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

(2016) Serkan Kiranyaz et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Deep learning approach for active classification of electrocardiogram signals

(2016) M.M. Al Rahhal et al.   INFORMATION SCIENCES

DIAGNOSIS OF MULTICLASS TACHYCARDIA BEATS USING RECURRENCE QUANTIFICATION ANALYSIS AND ENSEMBLE CLASSIFIERS

(2016) USHA DESAI et al.   Journal of Mechanics in Medicine and Biology

Deep learning

(2015) Yann LeCun et al.   NATURE

ECG Biometric with Abnormal Cardiac Conditions in Remote Monitoring System

(2014) Khairul A. Sidek et al.   IEEE Transactions on Systems Man Cybernetics-Systems

Application of higher order statistics for atrial arrhythmia classification

(2013) Roshan Joy Martis et al.   Biomedical Signal Processing and Control

A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification

(2010) Hamid Khorrami et al.   EXPERT SYSTEMS WITH APPLICATIONS

A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals

(2009) T. Ince et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING