Time-ResNeXt for epilepsy recognition based on EEG signalsin wireless networks

doi:10.21203/rs.2.23963/v2

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Time-ResNeXt for epilepsy recognition based on EEG signalsin wireless networks

https://doi.org/10.21203/rs.2.23963/v2

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published 07 Oct, 2020

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To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG)of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset), and has great potential for improving clinical practice.

Artificial Intelligence and Machine Learning

artificial intelligence

deep learning

epilepsy detection

Time-ResNeXt

Epilepsy is a brain disease that is caused by persistent susceptibility to recurrent seizures and the neurobiological, cognitive, psychological, and social consequences that result. According to estimates by the World Health Organization (WHO), about 2.4 million people worldwide are diagnosed with epilepsy every year [1]. Prolonged, frequent or severe seizures can lead to further brain damage and even persistent neuropsychiatric disorders. Sudden epilepsy (SUDEP) is a serious complication of epilepsy and is one of the most common causes of death in younger patients with epilepsy. The timely diagnosis of the presence and type of epilepsy is critical to its prognosis and choice of treatment options [2]. However, the diagnosis of epilepsy is relatively difficult, especially for the detection of seizures in newborns [3,4]. The usual clinical experience is judged by observing the behavior and other seizures of the newborn, but this is easily confused with other normal behaviors [5].

Epilepsy is often attributed to excessive abnormal discharges of neurons in the brain [6,7]. Electroencephalogram signals provide a powerful tool for the diagnosis of epilepsy. Experienced neuropathologists interpret EEG signals by observing the patterns of seizures and the period of seizures, and have formulated certain international standards to find specific signal characteristics in multi-channel electroencephalography [8,9]. Then, the condition of the patient is judged by the EEG signal rule that is manually explained. This method is relatively time-consuming and subjective, and it is objectively prone to errors [10,11]. Therefore, a suitable mechanism is needed to automatically interpret and classify EEG signals in patients with epilepsy.

EEG signal automatic classification methods usually use traditional manual feature machine learning and statistical methods, such as time-frequency analysis using wavelet transform [12], detection method using entropy estimator [13], discrete wavelet transform and approximate entropy Method [14] and so on. In addition, there are also methods for detecting using shallow neural networks using artificial features, such as Elman and Probabilistic Neural Networks [15], which use approximate entropy as input features of the network, artificial neural networks [16] The method using Volterra system and cellular nonlinear network [17] and so on.

With the development of the field of machine learning in recent years, a large number of excellent machine learning classification algorithms have emerged, the most representative of which is the deep neural network algorithm. Especially in the field of image classification, deep learning methods, such as VGG [18] network, Google Inception [19] network, and ResNet [20] network, have powerful automatic feature extraction capabilities [21], which have been completely completed in some fields Beyond traditional machine learning and statistical methods and shallow artificial neural network methods, it can even identify targets that are difficult to distinguish with the naked eye, surpassing humans. In addition, many large companies have also adopted the method of deep learning as one of their core competitiveness [22,23,24].

This paper draws on the excellent deep neural network structure in the image field, and designs an excellent end-to-end network structure based on ResNeXt [25] and suitable for EEG signal epileptic detection. And the performance of the network is verified on a public standard dataset (Berne Barcelona EEG dataset [26]), and compared with traditional algorithms [27,28,29,30] using this dataset, for us the performance of the algorithm is evaluated.

2.1 Data description

The data are from the EEG database of Berne Barcelona and are divided into two categories: EEG signal data during the onset of epilepsy patients and EEG signal data during the onset of epilepsy patients. Each category has 3750 pieces of data, each piece of data has 2 signal channels with a length of 10240 and a sampling frequency of 512Hz (the time length of each piece of data is 20s). Part of the original EEG image is shown in Fig. 1.

2.2 Training data preparation

Due to the balanced data classification, there is no data deviation. So there is no data enhancement for a single category. Only the data set is divided to prepare for model training. The method is shown in Table 1. The original data set is randomly divided into a training set (3000 items / category), a validation set (250 items / category), and a test set (500 items/category).

3.1 Model design ideas

ResNeXt's deep learning network model structure design idea is followed. According to the data characteristics of EEG signals, a network structure Time-ResNeXt is designed for EEG time series classification.

According to the traditional idea of designing network structures to improve the accuracy of the model, most of them are to deepen or widen the structure of the network, but as the number of hyperparameters (such as the number of channels, the size of the convolution kernel, etc.) increases, neural network design The difficulty and computational overhead also increase greatly. The algorithm in this paper benefits from the repeated topology of the ResNeXt network sub-modules, which enables it to have a very high accuracy rate while slightly increasing the amount of network calculations, while also greatly reducing the number of hyperparameters.

First, I have to mention the classic VGG network and Inception network. The design idea of VGG network is: modularize the neural network to increase the depth, but such a deep network will cause network degradation due to gradients. The structure of VGG network key modules is shown in Fig. 2.

The design philosophy of the Inception network is exactly the opposite: the width of the network is increased by the split-transform-merge method, but the settings of the various hyperparameters of this Inception network are more targeted and need to be performed when applied to other data sets. Many modifications, so scalability is average. The structure of the key modules of the Inception network is shown in Fig. 3.

The ResNeXt network is based on the design idea of ResNet's cross-layer connection, and combines the VGG and Inception networks. And through the structure of ResNet cross-layer connection to improve the shortcomings of VGG network too deep degradation. The cross-layer connection structure is shown in Fig. 4.

The transformation set structure is shown in Fig. 5.

The convolution modules of the transform set are all the same. ResNeXt uses a transformation set to replace the transformation structure of the Inception network. Because each aggregated topology is the same, the network no longer needs to modify too many hyperparameters on different data sets, which has better robustness.

3.2 Model design process

The original ResNeXt-50 has five stages and a large number of parameters, as shown in Fig. 6.

During training, it is found that the results are difficult to converge and tend to be completely random. Therefore, it was determined that the network structure was too complicated. Starting from the complexity of the network, the network was tailored to try to find a suitable structure. The test results are shown in Table 2.

3.3 Time-ResNeXt network structure

The structure of Time-ResNeXt neural network is shown in Table 3.

It has two phases in total. The detailed network structure of the first phase is shown in Fig. 7.

The depth of the second phase of the network structure is 2, that is, two network structure sub-modules, each of which contains cross-layer connections, activation layers, convolutional layers, batch normalization layers, and transform set modules. The main structure is The transformation set module, which uses a network design structure in a network, is a module for forming a convolution transformation set by connecting 32 convolutional structural blocks as shown in Fig. 8 in parallel, which is the main feature extraction module.

4.1 Optimizer

Use Adam's algorithm as the optimizer. The Adam algorithm is an algorithm that performs a stepwise optimization on a random objective function. This algorithm is based on adaptive low-order moment estimation, has high computational efficiency and low memory requirements. The adaptive learning rate of different parameters can be calculated by estimating the first and second gradients. In addition, the gradient rescaling of Adam's algorithm is invariant, so it is very suitable for solving problems with large-scale data or parameters.

The advantages are: easy to implement, efficient calculation, less memory required, invariance of gradient diagonal scaling, and only minimal tuning. The parameter settings of the Adam optimizer are shown in Table 4.

Among them, lr refers to the step size, that is, the step size of each gradient descent. Decay is a weight decay factor, which avoids overfitting by adding a regular term to the loss function.

4.2 Loss function and evaluation index

The loss function uses the cross entropy function of L2 regularization attenuation. The cross entropy function is calculated as follows.

See formulas 1, 2, and 3 in the supplementary files.

4.3 Early stop

In order to further avoid overfitting, an early stopping training strategy is adopted. When the model exceeds 30 consecutive generations of evaluation indicators and does not improve on the validation set, training is stopped. This can prevent the model from over-learning on the training set, avoid excessive bias, and reduce the generalization performance of the model.

4.4 Training process records

The results of the training set are shown in Fig. 9, the X-axis is the training algebra, and the Y-axis is the training evaluation index.

The results of the validation set are shown in Fig. 10.

The results show that at 74th generation, the model performs best on the validation data, with a correct rate of 0.9150.

Through continuous training of the model, the accuracy rate finally reached 0.9050, achieving an extremely advanced performance. Its pair is shown in Table 5.

In addition, there are many areas in this mission where you can continue to improve. For example, you can use Hard Example Mining to train difficult samples. Increasing the amount of data is also an extremely good method. The causes of epilepsy are complex. Trying more detailed multi-classification will help decouple the data and further reduce the difficulty of model training.

All in all, our classifier has achieved an extremely good performance and has excellent scalability on the Bern dataset in Barcelona. As the amount of data in real business scenarios increases, it will show even better performance.

Automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of epilepsy electroencephalogram (EEG), traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy of Time-ResNeXt in EEG epilepsy detection can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on a benchmark dataset , with great potential to improve clinical practice.

Acknowledgements

The authors acknowledged the anonymous reviewers and editors for their efforts in valuable comments and suggestions.

Funding

This work was supported by the National Natural Science Foundation of China (61873281, 61572522, 61502535, 61972416 and 61672248).

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Authors’ contributions

S.Q. Wang proposes the innovation ideas and theoretical analysis, and S.D. Wang carries out experiments and data analysis. Z. Song and Y.F.Wang conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

¹School of Computer and Communication Engineering ,China University of Petroleum(East of China),Qingdao 266000, P. R. China

²Department of Psychology,The Open University England Walton Hall Milton Keynes MK7 6AA

³Affiliated Hospital of Qingdao University Qingdao 266000, P. R. China

⁴Department of Traditional Chinese Medicine ,Shandong University of Traditional Chinese Medicine Jinan 250000, P. R. China

⁵ Harvard Medical School, Harvard University Cambridge, Massachusetts 02215

WHO: World Health Organization; FN:False Negative; FP:False Positive;TN:True Negative;TP:True Positive; TPR:True Positive Rate; FPR:False Positive Rate

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Table 1 Data allocation table

	training set	validation set	test set
Epilepsy signal	3000	250	500
Non-epileptic signal	3000	250	500
total	6000	500	1000

Table 2 Model optimization table

Included Network Phase	Network depth at each stage	Training result (correct rate)	Model parameter amount
First and second stage	-,1	0.8413	63,682
First and second stage	-,2	0.9050	132,802
First and second stage	-,3	0.8886	201,922
First second and third stage	-,2,1	0.8847	473,282

Table 3 Time-ResNeXt neural network structure

Resnext

7*7,64,stride 2

3*3 max pool,stride 2

Global average pool

1000-d fc,softmax

Table 4 Adam optimizer parameters

parameter name	Parameter value
Lr	0.001
beta_1	0.9
beta_2	0.999
Epsilon	1e-8
Decay	0.01

Table 5 Model comparison

Reference	Method	Accuracy (%)
Sharma et al. (2014)	Empirical modedecomposition (EMD)	87
M. Sharma et al. (2015)	Discrete wavelet transform (DWT)	84
AB. Das et al. (2016)	EMD-DWT	89.04
Bhattacharyya et al. (2018)	EME-DWT + SVM (50 pairs)	90.0
Our Method	Small ResNext on EEG	91.5

Other related evaluation indicators are shown in Table 6.

Table 6 Time-ResNeXt network evaluation

Evaluation index	Calculation formula		Corresponding value
Correct rate		0.9150
Specificity		0.8480
Recall		0.9620
Missed diagnosis rate		0.0380
Misdiagnosis rate		0.1520

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Journal Publication

published 07 Oct, 2020

Read the published version in EURASIP Journal on Wireless Communications and Networking →

Editorial decision: Accept
26 May, 2020
Review #2 received at journal
04 May, 2020
Review #1 received at journal
04 May, 2020
Review #3 received at journal
03 May, 2020
Reviewer #3 agreed at journal
01 May, 2020
Reviewers invited by journal
30 Apr, 2020
Reviewer #1 agreed at journal
30 Apr, 2020
Reviewer #2 agreed at journal
30 Apr, 2020
Editor assigned by journal
27 Apr, 2020
Submission checks completed at journal
26 Apr, 2020
Editor invited by journal
26 Apr, 2020

You are reading this latest preprint version

Time-ResNeXt for epilepsy recognition based on EEG signalsin wireless networks

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Abstract

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Introduction

Data Preparation

Network Model Design

Model Training

Results And Discussion

Conclusion

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Abbreviations

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