## 1 Introduction  
In recent years, the recurrent neural network (RNN) has been frequently used in time series data processing such as in medical information processing, etc. The RNN has a recursive structure inside and can handle variable lengths of input data.  
Generally, since it is difficult for a Simple RNN (Vanilla RNN) [1] with a simple structure to learn the time series data with long-term dependencies, two types of RNNs with complex gated structures to control the required information are proposed; they are long short-term memory (LSTM) [2, 3] and gated recurrent unit (GRU) [4], respectively. However, while the performance of RNNs with gated structures is improved, since back-propagation through time (BPTT) used for learning works by unrolling all input time steps, the more parameters there are in the RNN, the more memory is required and the higher the calculation costs. In order to solve this problem, many papers have been published that attempted to simplify LSTM and GRU [5, 6, 7, 8]. In our previous research, we proposed SGR (simple gated RNN) that uses the parts of gate structure of LSTM and GRU for Simple RNN to reduce parameters and realize faster learning [9]. In addition, we also proposed an RNN that introduced a new activation function \"hyperbolic secant (sech)\" into the gate [10], which is even simpler than Simple RNN (Vanilla RNN).  
The objective of this paper is to evaluate this RNN model with sech in the gate through a specific medical application task. In detail, the task here is to detect Parkinson's disease (PD) from the sound information of subjects. PD is the second most common neurodegenerative disease after [PERSON]'s disease [11]. The detection of PD is very important for medical treatments as well as improving the patient's quality of live (QOL). Focusing on the fact that patients with PD have motor speech disorders, here we ask subjects to pronounce a voice of /a/ for about 4 to 10 s to acquire data. In order to check the periodicity of the voice, the sound data are then converted into a recurrence plot (RP) at specific time intervals. A recurrence plot (RP) can visualize a periodic nature and chaos in time series. The generated RPs are set as input to a neural network model that combines the convolutional neural network (CNN) and RNN for classification.  
In the experiments, in order to evaluate the performance of the RNN with sech gate structure, we compared it with LSTM and GRU with conventional gate structures. We also compared the performance of this RNN when the used output activation function was changed between tanh and relu. For performance evaluation, accuracy, F1-score and Matthews correlation coefficient (MCC) were used. As a result, the proposed model obtained similar results to LSTM and GRU (an average accuracy of about 70%) with less hyperparameters, contributing to 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 outline of the paper is as follows: Section 2 discusses related works. Section 3 explains the framework of PD detection models and data preprocessing. The details of the experiments, the results, and discussion are described in Section 4. Section 5 is the conclusions.