This paper studies a novel recurrent neural network (RNN) with hyperbolic secant (sech) in the gate for a specific medical application task of Parkinson’s disease (PD) detection. In detail, it focuses on the fact that patients with PD have motor speech disorders, by converting the voice data into black-and-white images of a recurrence plot (RP) at specific time intervals and constructing the detection model that combines RNN and convolutional neural network (CNN); the study evaluates the performance of the RNN with sech gate compared with long short-term memory (LSTM) and gated recurrent unit (GRU) with conventional gates. As a result, the proposed model obtained similar results to LSTM and GRU (an average accuracy of about 70%) with less hyperparameters, resulting in faster learning. In addition, in the framework of the RNN with sech in gate, the accuracy obtained by using tanh as the output activation function is higher than using the relu function. The proposed method will see more improvement by increasing the data in the future. More analysis on the input sound type, the RP image size, and the deep learning structures will be included in our future work for further improving the performance of PD detection from voice.

中文翻译：

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应用帕金森病检测的双曲线割线RNN在闸门结构中的性能评估

本文研究了一种新型的带有双曲线割线（sech）的递归神经网络（RNN），用于帕金森氏病（PD）检测的特定医学应用任务。详细地讲，它通过将语音数据转换为特定时间间隔的重复图（RP）的黑白图像，并构建结合了RNN和卷积的检测模型，来关注PD患者患有运动性言语障碍这一事实。神经网络（CNN）; 这项研究评估了带有sech门的RNN与具有常规门的长短期记忆（LSTM）和门控循环单元（GRU）的性能。结果，所提出的模型以较少的超参数获得了与LSTM和GRU相似的结果（平均准确度约为70％），从而加快了学习速度。另外，在Rech和sech的框架中，使用tanh作为输出激活函数所获得的精度高于使用relu函数。将来通过增加数据量，所提出的方法将获得更多的改进。我们将在未来的工作中对输入声音类型，RP图像大小和深度学习结构进行更多分析，以进一步提高从声音检测PD的性能。