AIMC Topic: Heart Rate
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A Deep Learning Approach for Mental Fatigue State Assessment.
Sensors (Basel, Switzerland)
Jan 19, 2025
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neur...
Spiking-PhysFormer: Camera-based remote photoplethysmography with parallel spike-driven transformer.
Neural networks : the official journal of the International Neural Network Society
Jan 10, 2025
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, ...
Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients.
British journal of anaesthesia
Jan 8, 2025
BACKGROUND: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aime...
Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.
Computers in biology and medicine
Jan 7, 2025
BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variabi...
Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.
Scientific reports
Jan 6, 2025
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidi...
Rapid and accurate multi-phenotype imputation for millions of individuals.
Nature communications
Jan 4, 2025
Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been ...
Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning.
Computers in biology and medicine
Dec 27, 2024
Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimati...
Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.
Journal of cardiovascular electrophysiology
Dec 23, 2024
INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis co...
Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.
Journal of neuroengineering and rehabilitation
Dec 19, 2024
BACKGROUND: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outc...
Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.
BMC psychiatry
Dec 18, 2024
OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association betwe...
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