AIMC Topic: Clinical Alarms

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Reducing false arrhythmia alarm rates using robust heart rate estimation and cost-sensitive support vector machines.

Physiological measurement Jan 18, 2017
To lessen the rate of false critical arrhythmia alarms, we used robust heart rate estimation and cost-sensitive support vector machines. The PhysioNet MIMIC II database and the 2015 PhysioNet/CinC Challenge public database were used as the training d...
Humans Arrhythmias, Cardiac Heart Rate False Positive Reactions Costs and Cost Analysis Clinical Alarms Time Factors Electrocardiography Support Vector Machine Signal Processing, Computer-Assisted
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Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals.

Physiological measurement Jul 25, 2016
False arrhythmia alarms pose a major threat to the quality of care in today's ICU. Thus, the PhysioNet/Computing in Cardiology Challenge 2015 aimed at reducing false alarms by exploiting multimodal cardiac signals recorded by a patient monitor. False...
Machine Learning Intensive Care Units Electrocardiography Monitoring, Physiologic Signal Processing, Computer-Assisted Humans Arrhythmias, Cardiac Heart Rate False Positive Reactions Clinical Alarms
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Reduction of false arrhythmia alarms using signal selection and machine learning.

Physiological measurement Jul 25, 2016
In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy en...
Signal-To-Noise Ratio Clinical Alarms Arrhythmias, Cardiac Heart Rate Critical Care False Positive Reactions Humans Machine Learning Electrocardiography Monitoring, Physiologic Signal Processing, Computer-Assisted
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Suppression of false arrhythmia alarms in the ICU: a machine learning approach.

Physiological measurement Jul 25, 2016
This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of ...
Electrocardiography Monitoring, Physiologic Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Pattern Recognition, Automated False Positive Reactions Clinical Alarms Humans Machine Learning Intensive Care Units
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Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.

Journal of clinical monitoring and computing Oct 5, 2015
Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on ...
Hospital Information Systems Oscillometry Clinical Alarms Monitoring, Physiologic Blood Pressure Area Under Curve Data Interpretation, Statistical Data Mining Heart Rate Artifacts Risk Vital Signs Aged Adult Male Middle Aged Decision Support Systems, Clinical Humans Machine Learning Algorithms Female
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IoT-based incubator monitoring and machine learning powered alarm predictions.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary.
Humans Neural Networks, Computer Machine Learning Algorithms Infant, Newborn Humidity Cloud Computing Clinical Alarms Wireless Technology Incubators, Infant Temperature Mobile Applications Monitoring, Physiologic Internet of Things
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Development of an Alarm Algorithm, With Nanotechnology Multimodal Sensor, to Predict Impending Infusion Failure and Improve Safety of Peripheral Intravenous Catheters in Neonates.

Advances in neonatal care : official journal of the National Association of Neonatal Nurses Jun 1, 2020
BACKGROUND: Peripheral intravenous catheters connected to an infusion pump are necessary for the delivery of fluids, nutrition, and medications to hospitalized neonates but are not without complications. These adverse events contribute to hospital-ac...
Infusions, Intravenous Nanotechnology Infusion Pumps Clinical Alarms Humans Artificial Intelligence Patient Safety Infant, Newborn Catheterization, Peripheral Equipment Failure Monitoring, Physiologic
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Predict In-Hospital Code Blue Events using Monitor Alarms through 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
Bedside monitors in hospital intensive care units (ICUs) are known to produce excessive false alarms that could desensitize caregivers, resulting in delayed or even missed clinical interventions to life-threatening events. Our previous studies propos...
Cardiopulmonary Resuscitation Clinical Alarms Deep Learning Pilot Projects Monitoring, Physiologic
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Feasibility of Automated Propofol Sedation for Transcatheter Aortic Valve Implantation: A Pilot Study.

Anesthesia and analgesia Nov 1, 2017
BACKGROUND: Recently, several trials have shown that closed-loop sedation is feasible. No study has used automated sedation in extremely frail patients, such as those scheduled for transcatheter aortic valve implantation (TAVI). We developed and test...
Aged, 80 and over Male Hemodynamics Pilot Projects Monitoring, Intraoperative Prospective Studies Female Risk Factors Aged Robotics Elective Surgical Procedures Equipment Design Feasibility Studies Anesthetics, Intravenous Decision Support Systems, Clinical Treatment Outcome Propofol Hypnotics and Sedatives Humans Transcatheter Aortic Valve Replacement Consciousness Monitors Automation Respiration Anesthesia, Intravenous Clinical Alarms
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Technology: The Future of Agriculture.

Nature Apr 26, 2017
Automation Lasers Abattoirs Clinical Alarms Technology Transfer Depth Perception Crop Production Agrochemicals Fruit Quality Control Vegetables Pesticides Plant Diseases Monitoring, Physiologic Robotics Plant Weeds Remote Sensing Technology Machine Learning Soil Animals Livestock Animal Husbandry Animal Diseases Time Factors Mobile Applications Agriculture
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