## 5 Conclusions  
This paper evaluated the effectiveness of our RNN-SH model in a practical medical application of PD detection using RP with less parameters than other gated RNNs such as LSTM and GRU. RNN-SH can greatly reduce parameters, more so than other RNNs, with comparable accuracy for time series data processing although the difference in accuracy was small and a statistically significant difference was not found between RNN models.  
Since the gate parameter of RNN-SH is scalar, it is easy to analyze the result. In addition, another advantage is that the activation function can be easily changed according to the tasks. However, it turned out that it is difficult to improve the accuracy by simply replacing tanh with relu as the activation function in RNN-SH. From our experiment, the PD detection ability is lower using relu in comparison to using tanh, and we consider that dying relu might be the cause. It is necessary to analyze and make further improvements by considering parameter initialization and normalization methods, etc.  
Since the dataset size in our experiment was relatively small, 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. In regard to RP image compression, we would investigate an image size or a CNN structure that does not deteriorate the accuracy of PD detection in more detail to improve the current model.  
**Author Contributions:** Conceived and designed the model, [PERSON] and [PERSON]; performed the experiment and analyzed the results, [PERSON]; wrote the preliminary version of this manuscript, [PERSON]; revised the manuscript, [PERSON], C.Q., K.M., and S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
Figure 13: The examples of confusion matrix in first trial of RNN-SH. (**a**) is the confusion matrix of the RNN-SH (tanh), and (**b**) is that of the RNN-SH (relu). In first trial case, although the model could be learned relatively well when tanh is used, it could not be learned well when relu is used, and the PD detection ability was low.  
**Institutional Review Board Statement:** All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the \"Law of the People's Republic of China on Medical Practitioners\" (1998) declaration and its later amendments or comparable ethical standards.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Due to the nature of this research, participants of this study did not agree for their data to be shared publicly at present, so supporting data is not available.  
**Acknowledgments:** We wish to express our appreciation to the editor and reviewers for their insightful comments as well as the participants for supporting this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.