AIMC Topic: Heart Rate

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[Deep residual convolutional neural network for recognition of electrocardiogram signal arrhythmias].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Apr 25, 2019
Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations for different patients. Even for the same patient, its characteristics are variable under different...
Sensitivity and Specificity Electrocardiography Arrhythmias, Cardiac Heart Rate Humans Neural Networks, Computer Algorithms
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Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine Feb 1, 2019
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...
Machine Learning Female Reproducibility of Results Aged Humans Sepsis Hospital Mortality Systemic Inflammatory Response Syndrome ROC Curve Aged, 80 and over Male Middle Aged Severity of Illness Index Singapore Socioeconomic Factors Heart Rate Triage Organ Dysfunction Scores Emergency Service, Hospital
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Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Feb 1, 2019
AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-def...
Heart Rate Tachycardia, Ventricular Defibrillators, Implantable Electric Countershock Remote Sensing Technology Ventricular Fibrillation Signal Processing, Computer-Assisted Heart Failure Machine Learning Humans Databases, Factual Reproducibility of Results Risk Assessment Risk Factors Time Factors Treatment Outcome Predictive Value of Tests
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Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2019
BACKGROUND: Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure.
Heart Rate Sleep Stages Humans Algorithms Biomarkers Computer Simulation Electrocardiography Support Vector Machine
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Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Nov 1, 2018
AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from ...
Phenotype Retrospective Studies Reproducibility of Results Risk Factors Long QT Syndrome Time Factors Humans Heart Conduction System Genetic Predisposition to Disease Action Potentials Heart Rate Predictive Value of Tests Electrocardiography Signal Processing, Computer-Assisted Support Vector Machine
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Moving skin beyond the biological.

Nature Nov 1, 2018
Skin, Artificial Chromatophores Pressure Temperature Touch Animals Heart Rate Octopodiformes Wearable Electronic Devices Monitoring, Ambulatory Pliability Robotics Humans Silicon Prostheses and Implants Electromyography Electronics Biomimetics
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Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Oct 1, 2018
OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children.
Humans Heart Rate Artificial Intelligence Machine Learning Organ Dysfunction Scores Sepsis Respiratory Rate Adolescent Prospective Studies Logistic Models Child Female Monitoring, Physiologic Intensive Care Units, Pediatric Male Predictive Value of Tests Case-Control Studies
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Noise Detection in Electrocardiography Signal for Robust Heart Rate Variability Analysis: A Deep Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Heart rate variability (HRV) analysis is widely used to assess the sympathetic and parasympathetic tones. However, the quality of the derived HRV features is heavily dependent on the accuracy of QRS detection. Noisy electrocardiography (ECG) signals,...
Humans Electrocardiography Algorithms Support Vector Machine Deep Learning Signal Processing, Computer-Assisted Heart Rate
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Continuous Pain Intensity Estimation from Autonomic Signals with Recurrent Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Pain is usually measured by patient's self-report, which requires patient collaboration. Hence, the development of an objective automatic pain detection method would be useful in many clinical applications and patient populations. Previous studies ha...
Humans Neural Networks, Computer Pain Measurement Pain Heart Rate Galvanic Skin Response Autonomic Nervous System
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CHF Detection with LSTM Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Heart rate variability has been proven to be an effective prediction of risk of heart failure. The tradition method required manual feature extraction, thus may lead to potential error. In order to improve the robustness, a deep learning method based...
Heart Rate Neural Networks, Computer Deep Learning Heart Failure Humans
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