Investigation of the Effect of Adding Random Noise
to Noisy Biological Signals
on the Classification of Neural Network
Ryosuke Shimizu, Yoko Uwate and Yoshifumi Nishio
Dept. of Electrical and Electronic Engineering, Tokushima University
2-1 Minami-Josanjima, Tokushima 770--8506, Japan
Email:{shimizu, uwate, nishio}@ee.tokushima-u.ac.jp
Abstract— Time series classification is an important andchallenging problem in data analysis. Recently, time series
analysis using neural networks (NN) has attracted muchattention. However, the analysis of noisy time series data withcomplex oscillations is difficult. Therefore, it is important tosearch for effective features of the data. In this study, we add thedata to the noise and search for features suitable for NN
classification.

Figure 1. Image of Six hand movements.

Keywords; Neural Network,Time series ClassificationI.

INTRODUCTION

In recent years, there has been a lot of research on timeseries data analysis using neural networks (NN). The results
show that it is difficult to classify noisy data with NN.
Therefore, data preprocessing is important in NN. To solve thisproblem, this study generates randomly varying noise. The
signal-to-noise ratio of the noise is adjusted and added to thebiological signal. The signals to which the noise is added arethen classified using NN. The goal is to find features in thetime-series data with complex oscillations that are easy for theNN to learn the waveform.
II.

CONVOLUTIONAL NEURAL NETWORK

Convolutional Neural Network (CNN) is one of the NN andis mainly used in image recognition [1]. CNN is a modelwith a hierarchical structure that overlaps layers called
convolutional layers, and the accuracy has been improvedby deepening this hierarchical structure. In the 2015
competition, the Residual Network (ResNet) layered 152
layers and won the competition of the year. Now ResNet ispopular as one of the standard CNN models [2].

Figure 2. Waveform of Six hand movements.

IV. PROPOSED METHOD
In this study, we propose a method of feature translation forusing multidimensional features of time series data. Figure 3
shows the flow of the proposed method of this study.

III. DATASET
The data from the UCR repository [3] used in this study isan electromyogram (EMG) of hand movements. Five healthy
subjects (two males and three females) of approximately thesame age (20 to 22 years old) are asked to repeat the following
six movements that are considered to be basic hand movements
used in Fig. 2 [4]. An example of EMG time series data isshown in (Fig. 3).

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271①

The randomly varying noise is generated.

②

The signal-to-noise ratio is adjusted and noise is added
to the data.

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learning rate is the degree of learning progress. By setting as
shown in Table I, the training accuracies reach 100%.

Figure 3. Proposed method.

A)

1d-ResNet

TABLE I.

LEARNING PARAMETERS.

Batch size

Learning rate

Epoch420.0001

Early stopping

The Signal-to-Noise Ratio
VI. SIMULATION RESULTS

The SN ratio formula is used to find the RMS of noise thatwill give an arbitrary signal-to-noise ratio for speech. The
RMS of the noise to be obtained can be obtained bytransforming Eq. (1) and using Eq. (2). The ratio of theRMS(Nnoise) calculated in Eq. (2) to the RMS of the original
noise is calculated, and the amplitude of the original noise isadjusted by that ratio. Then add the amplitude of the adjusted
noise and the amplitude of the original data.

We investigate the average of 5 times of test accuracy. 1dResNet is used to investigate the accuracy of 6-value
classification. Table Ⅱ shows the test accuracy of the original
data and the highest and lowest accuracy of data by theproposed method. Although there are outliers, the accuracy
depends on the magnitude of the noise. The results also showthat there is an optimal value for NN depending on themagnitude of the signal-to-noise ratio. It was also found that
there is an optimal value for NN depending on the amplitudeof the signal-to-noise ratio in Fig. 6.

(1)
(2)

TABLE II.

LEARNING PARAMETERS
Test accuracy

V.

SIMURATION MODEL

In this study, the 1d-ResNet is used for classification model.

original

0.841

SN=100.871

SN=10.806

Figure 4. 1d-ResNet structure used in this study.

ResNet is commonly used for the classification of image
data. ResNet is the winner network of ILSVRC 2015, and it is
the network that enable to have deep layered networks without
vanishing gradient problem by using residual learning that isshown in Fig. 6. By inserting the shortcut connection that isexpressed by the longest arrow over 3 convolutional layers,previous information is learned, and it avoid vanishinggradient problem. In Fig. 5 and the following Fig. 6,Convolution (1×1, 128) indicates convolution layer with 1×1
filter and 128 channel.

Figure 6. Accuracy of proposed method.

VII. CONCLUSION
In this study, we checked how the accuracy of time seriesclassification by 1d-ResNet changes when noise is added tonoisy data. The results showed that the accuracy increased
with the size of the noise. We would like to investigate how
the accuracy changes when various types of noise are added.
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[3] Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen and G.
Batista. The UCR Time Series Classification Archive, 2015.
[4] T. S. Saponas, D. S. Tan, D. Morris, R. Balakrishnan, J. Turner, and JA.
Landay. Enabling Always-Available Input with Muscle-Computer Interfaces
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Association for Computing Machinery, New York, USA, pp.167-176, 2009.

Figure 5. The structure of residual learning.

Table I shows the batch size, epochs, and learning rate in thisstudy. Batch size is the amount of data processed at once. Theepoch is the number of the learnings. Simulation terminates
when val loss no longer decays during 1500 epochs. The

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