AIMC Topic: Arrhythmias, Cardiac

Clear Filters Showing 191 to 200 of 287 articles

ECG Multilead Interval Estimation Using Support Vector Machines.

Journal of healthcare engineering Apr 15, 2019
This work reports a multilead interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including detection as well as an accurate multilead interval detection algorithm using sup...
Heart Rate Analysis of Variance Support Vector Machine Signal Processing, Computer-Assisted Arrhythmias, Cardiac Software Algorithms Databases, Factual Diagnosis, Computer-Assisted Electrocardiography Cardiovascular Diseases Case-Control Studies Humans
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Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Nature medicine Jan 7, 2019
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning present an opportunity to substantially improve the accuracy and s...
Humans Electrocardiography Arrhythmias, Cardiac Neural Networks, Computer Algorithms Monitoring, Ambulatory ROC Curve Cardiologists
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Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types.

Computers in biology and medicine Dec 20, 2018
Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and complications, and are sometimes life-threatening. These conduction system perturbations can manifest ...
Humans Neural Networks, Computer Databases, Factual Signal Processing, Computer-Assisted Female Arrhythmias, Cardiac Male Electrocardiography
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A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation.

Journal of electrocardiology Nov 16, 2018
BACKGROUND: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and ST-T-U waves. We compared the accuracy of the first version of Cardiologs...
Emergency Service, Hospital Humans Arrhythmias, Cardiac Neural Networks, Computer Algorithms Electrocardiography Predictive Value of Tests Retrospective Studies Sensitivity and Specificity
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Applying machine learning to continuously monitored physiological data.

Journal of clinical monitoring and computing Nov 11, 2018
The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for moni...
Delirium Arrhythmias, Cardiac Atrial Fibrillation Decision Support Systems, Clinical Monitoring, Physiologic Randomized Controlled Trials as Topic Clinical Alarms Critical Care Expert Systems Wavelet Analysis Databases, Factual Intensive Care Units Electronic Health Records Adult Respiration, Artificial Sepsis Machine Learning Software Algorithms Humans
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ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.

Physiological measurement Sep 24, 2018
OBJECTIVE: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the gen...
Humans Electrocardiography Arrhythmias, Cardiac Neural Networks, Computer Pattern Recognition, Automated Sensitivity and Specificity Diagnosis, Computer-Assisted
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Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks.

IEEE journal of biomedical and health informatics Sep 20, 2018
This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points ...
Middle Aged Deep Learning Electrocardiography Heart Rate Signal Processing, Computer-Assisted Arrhythmias, Cardiac Young Adult Adult Male Female Aged Aged, 80 and over Humans
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Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.

Computers in biology and medicine Aug 4, 2018
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewe...
Arrhythmias, Cardiac Electrocardiography Support Vector Machine Signal Processing, Computer-Assisted Humans Neural Networks, Computer Databases, Factual
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Monitoring significant ST changes through deep learning.

Journal of electrocardiology Aug 1, 2018
Humans Algorithms Electrocardiography, Ambulatory Arrhythmias, Cardiac Sensitivity and Specificity Clinical Alarms Risk Assessment Deep Learning
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Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion.

IEEE transactions on bio-medical engineering Jul 11, 2018
OBJECTIVE: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenari...
Databases, Factual Signal Processing, Computer-Assisted Humans Electrocardiography Arrhythmias, Cardiac Neural Networks, Computer Blood Pressure Heart Rate Algorithms
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