Key result
HRV-based deep neural network distinguishes anaesthesia states with ~90% accuracy, outperforming traditional models.
Why the study?
Electroencephalogram-based depth of anaesthesia monitors are widely used but may be inaccurate under certain conditions.
Does a deep neural network using HRV-derived features improve accuracy in distinguishing anaesthesia states compared to traditional machine learning models in anaesthesia patients?
Observational (n=23)
No
Does a deep neural network using HRV-derived features improve accuracy in distinguishing anaesthesia states compared to traditional machine learning models in anaesthesia patients?
p-value: p=<0.05
A deep neural network using heart rate variability features can accurately distinguish anaesthesia states, outperforming traditional machine learning models.
May enhance anaesthesia monitoring; leaves open prospective validation before clinical adoption.
Background: Estimating 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, the hypothesis that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states was investigated.Methods: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived time and frequency domain features 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 used 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.Results: The 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.Conclusions: The incorporation of four HRV-derived time and frequency domain features and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot of a feasibility study, providing a method to supplement DoA monitoring based on EEG features to improve the accuracy of DoA estimation.
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Zhan et al. (2020) conducted an observational in General anaesthesia (n=23). Deep neural network using HRV-derived features vs. Logistic regression, support vector machine, and decision tree models was evaluated on Classification accuracy of anaesthesia states (p=<0.05). A deep neural network using four heart rate variability-derived features distinguished different anaesthesia states with 90.1% accuracy, significantly outperforming logistic regression, support vector machine, and decision tree models.
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