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In this study, a novel method is proposed to combine modified detrended fluctuation analysis (MDFA) and entropy to extract features of electroencephalogram (EEG), which are then processed using a random forest algorithm to generate a new DoA index. The bispectral index (BIS) was used as the reference standard. The proposed DoA index achieved Pearson and Spearman correlation coefficients of 0.97 (p < 0.01) and 0.95 (p < 0.01) with the BIS index, respectively. Additionally, the mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) were 20.45, 4.52, and 2.85, respectively. These results indicate that the proposed DoA index is more accurate in patients’ consciousness level assessment.

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DoA Assessment Based on EEG DFA and Entropy Features

  • 会议论文

  • 发表日期：

    2025年

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  • Xing Chen,
  • Bo Song,
  • Peng Wen

摘要

In this study, a novel method is proposed to combine modified detrended fluctuation analysis (MDFA) and entropy to extract features of electroencephalogram (EEG), which are then processed using a random forest algorithm to generate a new DoA index. The bispectral index (BIS) was used as the reference standard. The proposed DoA index achieved Pearson and Spearman correlation coefficients of 0.97 (p < 0.01) and 0.95 (p < 0.01) with the BIS index, respectively. Additionally, the mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) were 20.45, 4.52, and 2.85, respectively. These results indicate that the proposed DoA index is more accurate in patients’ consciousness level assessment.