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English(EN) Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNN

深度学习模型以93.6%的准确率预测rTMS抑郁症治疗结局

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的深度学习模型，用于预测重复经颅磁刺激（rTMS）抑郁症治疗的有效性。通过使用欧氏距离傅里叶-贝塞尔级数展开（FBSE-ED）将脑电图（EEG）信号转换为图像，所提出的卷积神经网络（CNN）实现了93.60%的分类准确率。该方法优于现有的特定于EEG的模型和预训练模型，表明其在精神科诊所中用于靶向临床决策和实际部署的潜力。 AI

影响 可能使接受rTMS治疗的抑郁症患者能够做出更精确、更及时的治疗决策。

排序理由 详细介绍一种用于特定医疗应用的深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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深度学习模型以93.6%的准确率预测rTMS抑郁症治疗结局

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详细介绍一种用于特定医疗应用的深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wael Korani, Md Fahimul Kabir Chowdhury, Sadam AlQadi, Priyan Malarvizhi kumar, Reza Rostami, Reza Kazemi ·

    使用脑电图信号和CNN预测rTMS抑郁治疗的结局

    arXiv:2607.22776v1 Announce Type: new Abstract: Repetitive transcranial magnetic stimulation (rTMS) is a non invasive therapy for Major Depressive Disorder (MDD). In this study, we generate images using two time frequency methods to represent EEG signals: Fourier-Bessel Series Ex…

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