ArticleBMC anesthesiology2021
Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.
Article in BMC anesthesiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and its clinical application in Anesthesiology: a systematic review.Journal of clinical monitoring and computing · 2024Pooled it
- Effect of different anesthesia depths on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia.BMC anesthesiology · 2024Trial
- Utility of the Perfusion Index as an Indicator of Anesthetic Depth during Total Intravenous Anesthesia in Neurosurgery with Intraoperative Neuromonitoring : A Prospective Observational Study.Journal of Korean Neurosurgical Society · 2026Article
- A Multitask Time-Frequency Deep Learning Approach for Anesthesia Depth Monitoring and Transition Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks.Sensors (Basel, Switzerland) · 2026Article
- A Machine Learning Approach for Real-Time Detection of Inadequate Sedation Using Non-EEG Physiological Signals.Bioengineering (Basel, Switzerland) · 2025Article
- Heart rate variability as a predictor of intraoperative autonomic nervous system homeostasis.Journal of clinical monitoring and computing · 2024Article
- Exploring Heart Rate Variability Biofeedback as a Nonpharmacological Intervention for Enhancing Perioperative Care: A Narrative Review.Turkish journal of anaesthesiology and reanimation · 2024Article
- Article
- Heart rate variability in anaesthesiology - narrative review.Anaesthesiology intensive therapy · 2023Review
- Application of Decision Tree Intelligent Algorithm in Data Analysis of Physical Health Test.Journal of healthcare engineering · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundEstimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.
methodsA novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.
resultsThe accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.
conclusionsThe incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method-with other evaluation methods, such as EEG-is expected to assist anaesthesiologists in the accurate evaluation of the DoA.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.