Performance Evaluation of RNN with Hyperbolic Secant in Gate Structure through Application of Parkinson’s Disease Detection

Tomohiro Fujita,Sheng Cao,Zhiwei Luo,Changqin Quan,Kohei Mori

doi:10.3390/app11104361

Abstract

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.

Highlights

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. 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
While the performance of RNNs with gated structures is improved, since backpropagation 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

Summary

Introduction

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. 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.

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Discussion

Conclusion