通过伽玛波段有效连接对正念体验进行分类:将机器学习算法应用于静息、呼吸和身体扫描。

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ai-Ling Hsu , Chun-Yu Wu , Hei-Yin Hydra Ng , Chun-Hsiang Chuang , Chih-Mao Huang , Changwei W. Wu , Yi-Ping Chao
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引用次数: 0

摘要

背景和目的:正念是一种心理过程,旨在通过改变大脑功能来实现互感意识、减轻压力和调节情绪。文献显示,脑电图(EEG)得出的连通性具有区分正念天真者和正念体验者大脑功能的潜力,这种定量区分有利于心理健康的远程诊断。然而,目前还没有针对正念经验预测的模型选择指南。在此,我们假设脑电图有效连通性可以在正念体验中达到良好的预测效果,并具有大脑可解释性:方法:我们旨在利用直接定向传递函数(dDTF)对参与者的正念减压(MBSR)历史进行分类,并通过比较多种机器学习(ML)算法来优化预测准确性。我们以伽玛波段有效连通性为目标,在7种机器学习(ML)算法和3个环节(即静息、专注-呼吸和身体扫描)中评估了基于脑电图的正念体验预测:支持向量机和天真贝叶斯分类器在所有三个环节中都表现出显著的准确率,高于偶然水平,与其他两种正念状态的分类准确率相比,决策树算法在静息状态下的预测准确率最高,达到 91.7%。我们进一步对重要的脑电图通道进行了分析,以保持分类的准确性,结果显示,在 19 个通道中只保留了 4 个通道(F7、F8、T7 和 P7),准确率就达到了 83.3%。深入分析连接特征的贡献,主要位于额叶的特定连接特征对分类器的构建贡献更大,这与现有的正念文献非常吻合:在本研究中,我们开发了一种基于脑电图的分类器来客观检测一个人的正念体验,这是一个里程碑。利用本地静息态脑电数据,决策树的预测准确率达到了区分正念体验的最佳水平。建议的正念体验预测算法和关键通道可为未来嵌入可穿戴神经反馈系统或可信数字疗法中的基于脑电图的分类法预测正念体验提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan

Background and Objective

Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability.

Methods

We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants’ history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan).

Results

The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature.

Conclusion

In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics.
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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