Article

Heart rate fragmentation improves general anesthesia state classification using machine learning

2025-01-08

Abstract excerpt

Accurate assessment of consciousness during general anesthesia is crucial for optimizing anesthetic dosage and patient safety. Current electroencephalogram-based monitoring devices can be inaccurate or unreliable in specific surgical contexts ( e . g . cephalic procedures). This study investigated the feasibility of using electrocardiogram (ECG) features and machine learning to differentiate between awake and anes...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: c38e6ec1-87e9-5e55-976d-e43d12ae2642
DOI: 10.1101/2025.01.07.25320157

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Heart rate fragmentation improves general anesthesia state classification using machine learningDOI 10.1101/2025.01.07.25320157Read

Repurposing EEG monitoring of general anaesthesia for building biomarkers of brain ageing: An exploratory studySemantic proximity Read

Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousnessSemantic proximity Read

Electroencephalogram Monitoring of Depth of Anesthesia during Office-Based AnesthesiaSemantic proximity Read

Mapping general anesthesia states based on electro-encephalogram transition phasesSemantic proximity Read

Multimodal Seizure Detection with Long-term Ambulatory ECG and Accelerometry DataSemantic proximity Read

Reduced neural activity during volatile anesthesia compared to TIVA: evidence from a novel EEG signal processing analysisSemantic proximity Read

Bayesian heart-rate entropy identifies autonomic dynamics during seizure evolution: a reproducible pilot studySemantic proximity Read

Assessment of Anesthetic Depth Through EEG Mode Decomposition Using Singular Spectrum AnalysisSemantic proximity Read

Patterns and Diagnostic Utility of Electroencephalography (EEG) in a Secondary Hospital: A Retrospective Study from Saudi ArabiaSemantic proximity Read

Predicting sensitivity to general anesthesia: Bispectral index versus Checkpoint-Decomposition AlgorithmSemantic proximity Read

Development of a Transformer-Based Cardiac Arrest Prediction Model for General Ward PatientsSemantic proximity Read

Complexity measures of the electroencephalograph capture loss and recovery of consciousness in patients anesthetized with propofolSemantic proximity Read