AIMC Topic: Heart Rate
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Dynamic HRV Monitoring and Machine Learning Predict NYHA Improvements in Acute Heart Failure Patients.
Computers in biology and medicine
Mar 9, 2025
Heart failure (HF) is marked by significant morbidity, mortality, and readmission rates, highlighting a critical need for accurate assessment of treatment efficacy. The New York Heart Association (NYHA) classification, while standard, falls short in ...
Deep reinforcement learning for multi-targets propofol dosing.
Journal of clinical monitoring and computing
Mar 6, 2025
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...
Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.
Journal of psychiatric research
Mar 4, 2025
Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is unclear. Given that depression disrupts circadian rhythms, assessing HRV fluctuations may better capt...
Classifying metro drivers' cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination.
Scientific reports
Mar 4, 2025
Metro drivers are more likely to trigger accidents if they suffer from cognitive distractions during manual driving. However, identifying metro drivers' cognitive distractions faces challenges as generally no obvious behavior can be found during the ...
Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment.
Scientific reports
Mar 4, 2025
Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health problems and socioeconomic impairments. Although the complex symptomatology of PTSD makes diagnosis diff...
Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.
BMC medical informatics and decision making
Mar 3, 2025
BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease and perform a single-lead ECG, but its reliability and validity among pregnant women is unclear. The...
Investigation of Inter-Patient, Intra-Patient, and Patient-Specific Based Training in Deep Learning for Classification of Heartbeat Arrhythmia.
Cardiovascular engineering and technology
Feb 26, 2025
Effective diagnosis of electrocardiogram (ECG) is one of the simplest and fastest ways to assess the heart's function. In the recent decade, various attempts have been made to automate the classification of electrocardiogram signals to detect heartbe...
Deep Learning Approach for Automatic Heartbeat Classification.
Sensors (Basel, Switzerland)
Feb 25, 2025
Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional ...
Diastolic Versus Systolic or Mean Intraoperative Hypotension as Predictive of Perioperative Myocardial Injury in a White-Box Machine-Learning Model.
Anesthesia and analgesia
Feb 20, 2025
BACKGROUND: Intraoperative hypotension (IOH) and tachycardia are associated with perioperative myocardial injury (PMI), and thereby increased postoperative mortality. Patients undergoing vascular surgery are specifically at risk of developing cardiac...
KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate From a Smartwatch.
IEEE transactions on bio-medical engineering
Feb 20, 2025
Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they of...
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