AIMC Topic: Monitoring, Physiologic

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Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

Sensors (Basel, Switzerland) Dec 15, 2016
Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high...
Humans Monitoring, Physiologic Posture Joints Human Body Orientation Adult Biomechanical Phenomena Male Time Factors Neural Networks, Computer Machine Learning Algorithms Female
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A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series.

IEEE journal of biomedical and health informatics Dec 7, 2016
Physiological variables, such as heart rate (HR), blood pressure (BP) and respiration (RESP), are tightly regulated and coupled under healthy conditions, and a break-down in the coupling has been associated with aging and disease. We present an appro...
Young Adult Adult Male Humans Models, Statistical Machine Learning Aging Area Under Curve Algorithms Databases, Factual Signal Processing, Computer-Assisted Middle Aged Female Critical Care Sepsis Vital Signs Aged Baroreflex Aged, 80 and over Monitoring, Physiologic
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An intelligent prognostic system for analyzing patients with paraquat poisoning using arterial blood gas indexes.

Journal of pharmacological and toxicological methods Nov 21, 2016
The arterial blood gas (ABG) test is used to assess gas exchange in the lung, and the acid-base level in the blood. However, it is still unclear whether or not ABG test indexes correlate with paraquat (PQ) poisoning. This study investigates the predi...
Female Aged Young Adult Adult Humans Machine Learning Algorithms Prognosis Paraquat Herbicides Blood Gas Analysis Male Middle Aged Adolescent Monitoring, Physiologic
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A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs.

Computer methods and programs in biomedicine Oct 27, 2016
BACKGROUND AND OBJECTIVES: In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly cor...
Awareness Humans Algorithms Clinical Decision-Making Monitoring, Physiologic
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Ambient intelligence for health environments.

Journal of biomedical informatics Oct 18, 2016
Humans Artificial Intelligence Monitoring, Physiologic Robotic Surgical Procedures Environment Wireless Technology
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A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.

Physiological measurement Sep 28, 2016
This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications....
Humans Machine Learning Electrocardiography Movement Monitoring, Physiologic Signal Processing, Computer-Assisted Heart Rate Determination Artifacts Statistics as Topic
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Machine learning approaches to personalize early prediction of asthma exacerbations.

Annals of the New York Academy of Sciences Sep 14, 2016
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been system...
Combined Modality Therapy Telemedicine Monitoring, Physiologic Bayes Theorem Internet Models, Biological Severity of Illness Index Algorithms ROC Curve Precision Medicine Decision Support Systems, Clinical Prognosis Computational Biology Electronic Health Records Adult Humans Self Care Machine Learning Asthma Disease Progression
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Cognitive bio-radar: The natural evolution of bio-signals measurement.

Journal of medical systems Aug 30, 2016
In this article we discuss a novel approach to Bio-Radar, contactless measurement of bio-signals, called Cognitive Bio-Radar. This new approach implements the Bio-Radar in a Software Defined Radio (SDR) platform in order to obtain awareness of the en...
Monitoring, Physiologic Signal Processing, Computer-Assisted Feasibility Studies Respiratory Rate Radar Artificial Intelligence
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Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

PloS one Aug 17, 2016
INTRODUCTION: Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used r...
Adolescent Cohort Studies Models, Theoretical Algorithms Treatment Outcome Prognosis Humans Heart Arrest Monitoring, Physiologic Adult Retrospective Studies Neural Networks, Computer Female Critical Care Hematologic Neoplasms Electronic Health Records Machine Learning Early Diagnosis Aged Male Vital Signs Aged, 80 and over Middle Aged Young Adult
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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...
Heart Rate False Positive Reactions Clinical Alarms Electrocardiography Monitoring, Physiologic Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Machine Learning Intensive Care Units
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