AIMC Topic: Dyspnea

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Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate.

Scientific reports May 21, 2025
Rapid respiratory rate (RR) changes in older adults may indicate serious illness. Therefore, accurately estimating RR for cardiorespiratory fitness is essential. However, machine learning algorithm-related errors are unsuitable for medical decision-m...
Female Aged Male Humans Machine Learning Algorithms Middle Aged Dyspnea Respiratory Rate
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Pathophysiological mechanisms of exertional dyspnea in people with cardiopulmonary disease: Recent advances.

Respiratory physiology & neurobiology Mar 29, 2025
Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, there has been a renewed interest in uncovering the mechanisms underlying this distressing symptom. We start by articulating a conceptual framework linki...
Humans Exercise Lung Diseases Dyspnea
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Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.

Expert review of respiratory medicine Mar 2, 2025
BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise that is often not available. We aim to evaluate the utility of ChatGPT (GPT) in interpreting CPET res...
Male Exercise Tolerance Middle Aged Dyspnea Lung Aged Predictive Value of Tests Adult Oxygen Consumption Cross-Sectional Studies Exercise Test Humans Female Reproducibility of Results
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Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal.

Sensors (Basel, Switzerland) Feb 26, 2025
Respiratory rate (RR) changes in the elderly can indicate serious diseases. Thus, accurate estimation of RRs for cardiopulmonary function is essential for home health monitoring systems. However, machine learning (ML) algorithm errors embedded in hea...
Photoplethysmography Dyspnea Monitoring, Physiologic Humans Female Signal Processing, Computer-Assisted Respiratory Rate Aged Uncertainty Machine Learning Male Algorithms
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Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.

BMC emergency medicine Jan 5, 2025
BACKGROUND: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The ri...
Female Sweden Aged Humans Machine Learning Retrospective Studies Middle Aged Emergency Medical Services Triage Dyspnea Aged, 80 and over Adult Male
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A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.

PloS one Dec 27, 2024
BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart...
Pulmonary Disease, Chronic Obstructive Cross-Sectional Studies Male Middle Aged Deep Learning Humans Aged Emergency Service, Hospital Pneumonia Aged, 80 and over Heart Failure Adult Delivery of Health Care Dyspnea Female Sweden
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Impact of upper extremity robotic rehabilitation on respiratory parameters, functional capacity and dyspnea in patients with stroke: a randomized controlled study.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology Nov 11, 2024
BACKGROUND: Stroke leads to reduced mobility and functional capacity, also negatively affects respiratory functions and muscle strength.
Stroke Dyspnea Respiratory Function Tests Upper Extremity Stroke Rehabilitation Respiratory Muscles Treatment Outcome Muscle Strength Robotics Male Middle Aged Humans Adolescent Young Adult Adult Female Aged
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Artificial intelligence as a further step in the detection of dyspnea in the critically ill mechanically ventilated patient.

Intensive care medicine May 2, 2024
Respiration, Artificial Dyspnea Humans Artificial Intelligence Critical Illness
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Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.

Sensors (Basel, Switzerland) Sep 1, 2023
A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform de...
Dyspnea Humans Records Robotics
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Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.

Sensors (Basel, Switzerland) Feb 22, 2023
In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were des...
Deep Learning Noise Humans Dyspnea Telephone
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