A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features
A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features
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DOI：
10.1016/j.jneumeth.2016.07.012
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发表时间：
2016-09-15
影响因子：
3
通讯作者：
Bhuiyan, Mohammed Imamul Hassan
中科院分区：
文献类型：
--
作者：
Hassan, Ahnaf Rashik;Bhuiyan, Mohammed Imamul Hassan
背景资料：自动睡眠评分至关重要，因为传统上医生必须对大量数据进行可视化分析，这是繁重、耗时且容易出错的。因此，迫切需要一个自动的睡眠分期方案。新方法：在这项工作中，我们分解睡眠EEG信号段使用可调Q因子小波变换（TQWT）。然后从TQWT子带计算各种频谱特征。在TQWT域的频谱特征的性能已被确定的直观和图形分析，统计验证，和Fisher准则。随机森林用于执行分类。TQWT和随机森林参数的最佳选择和效果已经确定和explained.Results：实验结果表明，我们的功能生成方案的方差分析和Fisher标准的p值方面的功效。该方案在Sleep-EDF数据集上对睡眠状态的6阶段到2阶段分类的收益率分别为90.38%、91.50%、92.11%、94.80%、97.50%。与现有方法的比较：在准确率和Cohen's kappa系数方面，该方法的性能明显优于现有方法。此外，该方案给出了高的检测精度的睡眠阶段非REM 1和REM。结论：TQWT域的频谱特征可以区分对应于不同的睡眠状态的睡眠脑电信号有效。该计划将减轻医生的负担，加快睡眠障碍的诊断，并加快睡眠研究。(C)© 2016 Elsevier B. V.版权所有。
Background: Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme.New method: In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded.Results: Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising.Comparison with existing methods: The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM.Conclusions: Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research. (C) 2016 Elsevier B.V. All rights reserved.