Related references
Note: Only part of the references are listed.A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
Mengze Wu et al.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2021)
Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review
Igor Matias et al.
COMPUTER SCIENCE REVIEW (2021)
A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks
Muhammad Muzammal et al.
INFORMATION FUSION (2020)
Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms
Vessela Krasteva et al.
SENSORS (2020)
Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Shenda Hong et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2020)
Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation A systematic review
Angela Hall et al.
MEDICINE (2020)
Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis
Pramendra Kumar et al.
HEALTHCARE TECHNOLOGY LETTERS (2020)
Deep-ECG: Convolutional Neural Networks for ECG biometric recognition
Ruggero Donida Labati et al.
PATTERN RECOGNITION LETTERS (2019)
Segmentation of the ECG Signal by Means of a Linear Regression Algorithm
Javier Aspuru et al.
SENSORS (2019)
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Awni Y. Hannun et al.
NATURE MEDICINE (2019)
Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal
Wei Zhao et al.
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) (2019)
Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications
Wanqing Wu et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2018)
A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms
Philipp Sodmann et al.
PHYSIOLOGICAL MEASUREMENT (2018)
Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG
Ivaylo Christov et al.
PHYSIOLOGICAL MEASUREMENT (2018)
Identifying Normal, AF and other Abnormal ECG Rhythms using a Cascaded Binary Classifier
Shreyasi Datta et al.
2017 COMPUTING IN CARDIOLOGY (CINC) (2017)
Deep Learning for ECG Classification
B. Pyakillya et al.
BIGDATA CONFERENCE (FORMERLY INTERNATIONAL CONFERENCE ON BIG DATA AND ITS APPLICATIONS) (2017)
ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM
Sandeep Raj et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2017)
AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017
Gari D. Clifford et al.
2017 COMPUTING IN CARDIOLOGY (CINC) (2017)
Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy
Marta Carrara et al.
PHYSIOLOGICAL MEASUREMENT (2015)
Prediction and personalised treatment of atrial fibrillation-stroke prevention: consolidated position paper of CVD professionals
Thomas M. Helms et al.
EPMA JOURNAL (2014)
Cognitive Impairment Associated With Atrial Fibrillation A Meta-analysis
Shadi Kalantarian et al.
ANNALS OF INTERNAL MEDICINE (2013)
Subclinical Atrial Fibrillation and the Risk of Stroke
Jeff S. Healey et al.
NEW ENGLAND JOURNAL OF MEDICINE (2012)
Common errors in computer electrocardiogram interpretation
ME Guglin et al.
INTERNATIONAL JOURNAL OF CARDIOLOGY (2006)
PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals
AL Goldberger et al.
CIRCULATION (2000)
Share Paper
