AIMC Topic: Respiratory Rate

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A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.

Proceedings of the National Academy of Sciences of the United States of America
Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or fi...

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

Scientific reports
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...

Video-Based Multiphysiological Disentanglement and Remote Robust Estimation for Respiration.

IEEE transactions on neural networks and learning systems
Remote noncontact respiratory rate estimation by facial visual information has great research significance, providing valuable priors for health monitoring, clinical diagnosis, and anti-fraud. However, existing studies suffer from disturbances in epi...

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.

Sensors (Basel, Switzerland)
Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard" for pain assessment, tools are needed to objectively monitor and account for inter-individual diff...

Deep reinforcement learning for multi-targets propofol dosing.

Journal of clinical monitoring and computing
The administration of propofol for sedation or general anesthesia presents challenges due to the complex relationship between patient factors and real-time physiological responses. This study explores the application of deep reinforcement learning (D...

Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal.

Sensors (Basel, Switzerland)
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...

Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation.

Computers in biology and medicine
Wearable technology has expanded the applications of photoplethysmography (PPG) in remote health monitoring, enabling real-time measurement of various physiological parameters, such as heart rate (HR), heart rate variability (HRV), and respiration ra...

Predictive models for heat stress assessment in Holstein dairy heifers using infrared thermography and machine learning.

Tropical animal health and production
Heat stress is a condition that impairs the animal's productive and reproductive performance, and can be monitored by physiological and environmental variables, including body surface temperature, through infrared thermography. The objective of this ...

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sensors (Basel, Switzerland)
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...

Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.

Scientific reports
This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia betwee...