AIMC Topic: Monitoring, Physiologic
Clear Filters Showing 211 to 220 of 387 articles
Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.
Innovations (Philadelphia, Pa.)
Feb 28, 2020
In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances ...
A proposed health monitoring system using fuzzy inference system.
Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Feb 20, 2020
Due to the busy schedule of every human being in today's world, consciousness towards one's health has become quite alarming. A person suffering from any chronic disease needs a gradual, regular and close monitoring to recover from the disease or to ...
Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms.
ACS sensors
Jan 23, 2020
Mental fatigue, characterized by subjective feelings of "tiredness" and "lack of energy", can degrade individual performance in a variety of situations, for example, in motor vehicle driving or while performing surgery. Thus, a method for nonintrusiv...
Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows.
Animal : an international journal of animal bioscience
Jan 13, 2020
Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to chang...
Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.
Scientific reports
Jan 13, 2020
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patien...
Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.
PloS one
Jan 10, 2020
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multi- modal biosignals. Most of the current work in the literature are eithe...
Multi-Modal Diagnosis of Infectious Diseases in the Developing World.
IEEE journal of biomedical and health informatics
Jan 9, 2020
In low and middle income countries, infectious diseases continue to have a significant impact, particularly amongst the poorest in society. Tetanus and hand foot and mouth disease (HFMD) are two such diseases and, in both, death is associated with au...
A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity.
IEEE journal of biomedical and health informatics
Dec 31, 2019
OBJECTIVE: This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM) system.
Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning.
Physiological measurement
Dec 2, 2019
UNLABELLED: Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospi...
Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation.
Journal of medical Internet research
Nov 26, 2019
BACKGROUND: Informed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert ...
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