College of International Exchange, Shandong Management University, Jinan 250357, China.
Comput Intell Neurosci. 2022 Aug 21;2022:9208607. doi: 10.1155/2022/9208607. eCollection 2022.
In order to evaluate the therapeutic effect of music therapy on patients with depression, this paper proposes a CNN-based noise detection method with the combination of HHT and FastICA for noise removal, with good data support from the DBN model. DBN-based feature extraction and classification are completed. As the training process of DBN itself requires a large number of training samples, there are also disadvantages such as slow convergence speed and easy to fall into local minima, which lead to a large amount of effort and time, and the learning efficiency is relatively low. A DBN optimization algorithm based on artificial neural network was proposed to evaluate the efficacy of music therapy. First of all, through the comparison of music therapy experimental group and control group, to verify that music therapy is effective for the treatment of depressed patients. Secondly, we propose to optimize the selection of features based on the frequency band energy ratio and the sliding average sample entropy, respectively, and then to classify the EEG of depressed patients under different music perceptions by training the DBN model and continuously adjusting the parameters, combined with the surtax classifier, and the classification accuracy is high. In particular, it can detect the different effects of different music styles, which is of great significance for the selection of appropriate music for the treatment of depressed patients.
为了评估音乐疗法对抑郁症患者的治疗效果，本文提出了一种基于 CNN 的噪声检测方法，结合 HHT 和 FastICA 进行噪声去除，具有 DBN 模型的良好数据支持。基于 DBN 的特征提取和分类完成。由于 DBN 本身的训练过程需要大量的训练样本，因此也存在收敛速度慢、容易陷入局部最小值等缺点，导致工作量大、时间长，学习效率相对较低。提出了一种基于人工神经网络的 DBN 优化算法，以评估音乐疗法的疗效。首先，通过音乐治疗实验组和对照组的比较，验证音乐治疗对治疗抑郁症患者是有效的。其次，我们提出了分别基于频带能量比和滑动平均样本熵的特征选择优化，并通过训练 DBN 模型不断调整参数，结合超税分类器对不同音乐感知下的抑郁症患者 EEG 进行分类，分类准确率高。特别是可以检测不同音乐风格的不同效果，这对于选择适合治疗抑郁症患者的音乐具有重要意义。
