AIMC Topic: Monitoring, Physiologic
Clear Filters Showing 181 to 190 of 387 articles
Acute hyperglycaemia in cystic fibrosis pulmonary exacerbations.
Endocrinology, diabetes & metabolism
Nov 30, 2020
BACKGROUND: Hyperglycaemia may contribute to failure to recover from pulmonary exacerbations in cystic fibrosis (CF). We aimed to evaluate the prevalence and mechanism of hyperglycaemia during and post-exacerbations.
Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Nov 30, 2020
BACKGROUND: Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools.
Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.
Chinese journal of traumatology = Zhonghua chuang shang za zhi
Nov 24, 2020
PURPOSE: The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse pr...
Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.
Sensors (Basel, Switzerland)
Nov 16, 2020
To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. How...
Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network.
Sensors (Basel, Switzerland)
Nov 10, 2020
The objective of this study was to investigate the accuracy of a Deep Neural Network (DNN) in recognizing activities typical for hospitalized patients. A data collection study was conducted with 20 healthy volunteers (10 males and 10 females, age = 4...
Machine learning enables improved runtime and precision for bio-loggers on seabirds.
Communications biology
Oct 30, 2020
Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspe...
Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System.
Journal of healthcare engineering
Oct 26, 2020
With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In th...
An attempt at estrus detection in cattle by continuous measurements of ventral tail base surface temperature with supervised machine learning.
The Journal of reproduction and development
Oct 9, 2020
We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cat...
Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model.
Sensors (Basel, Switzerland)
Sep 30, 2020
Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very val...
Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning.
Sensors (Basel, Switzerland)
Sep 25, 2020
A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activ...
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