[논문 리뷰] Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis

Palani Thanaraj Krishnan, B. Parvathavarthini외 4명|arXiv (Cornell University)|2020. 03. 08.

EEG and Brain-Computer Interfaces참고 문헌 42인용 수 42

한 줄 요약

논문은 EEG 에포크를 Gramian Angular Summation Field 이미지로 변환하고 전이 학습과 특징 기반 ANN으로 뇌전증 탐지를 위한 딥 CNN들(커스텀 CNN 및 사전 학습된 AlexNet, VGG16, VGG19)을 평가하여 GASF 표현에서 강력한 지표를 얻는다.

ABSTRACT

This paper evaluates the approach of imaging timeseries data such as EEG in the diagnosis of epilepsy through Deep Neural Network (DNN). EEG signal is transformed into an RGB image using Gramian Angular Summation Field (GASF). Many such EEG epochs are transformed into GASF images for the normal and focal EEG signals. Then, some of the widely used Deep Neural Networks for image classification problems are used here to detect the focal GASF images. Three pre-trained DNN such as the AlexNet, VGG16, and VGG19 are validated for epilepsy detection based on the transfer learning approach. Furthermore, the textural features are extracted from GASF images, and prominent features are selected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, a Custom Convolutional Neural Network (CNN) with three CNN layers, Batch Normalization, Max-pooling layer, and Dense layers, is proposed for epilepsy diagnosis from GASF images. The results of this paper show that the Custom CNN model was able to discriminate against the focal and normal GASF images with an average peak Precision of 0.885, Recall of 0.92, and F1-score of 0.90. Moreover, the Area Under the Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve is 0.92 for the Custom CNN model. This paper suggests that Deep Learning methods widely used in image classification problems can be an alternative approach for epilepsy detection from EEG signals through GASF images.

연구 동기 및 목표

• Gramian Angular Field 표현을 사용하여 시간 순 EEG 데이터의 뇌전증 진단 이미징을 동기 부여한다.
• GASF EEG 이미지에서 사전 학습된 DNN(AlexNet, VGG16, VGG19)을 활용한 전이 학습을 평가한다.
• GASF 이미지로부터 뇌전증 진단을 위한 커스텀 CNN 아키텍처를 개발한다.
• GASF 이미지에서 텍스처 특징을 탐색하고 선택된 특징들에 대해 ANN 분류기를 사용한다.

제안 방법

• 정상 및 focal EEG 신호에 대해 EEG 에포크를 RGB GASF 이미지로 변환한다.
• AlexNet, VGG16, VGG19를 사용한 전이 학습을 적용하여 GASF 이미지를 분류한다.
• GASF 이미지에서 텍스처 특징을 추출하고 다층 ANN 분류기에 사용할 눈에 띄는 특징들을 선택한다.
• 분류를 위한 세 개의 합성곱 층, Batch Normalization, Max-pooling 및 Dense 층을 포함하는 커스텀 CNN을 제안한다.
• 정밀도, 재현율, F1-스코어 및 AUC-ROC와 같은 지표를 사용하여 성능을 평가한다.

실험 결과

연구 질문

• RQ1 Can GASF representations of EEG signals enable effective epilepsy detection using image-based deep learning models?
• RQ2 How do pre-trained CNNs (AlexNet, VGG16, VGG19) perform on GASF-based epilepsy detection compared to a custom CNN?
• RQ3 Do textural features from GASF images improve epilepsy classification via a neural classifier?
• RQ4 What is the diagnostic performance (precision, recall, F1, AUC) of the proposed methods on GASF EEG images?

주요 결과

• The custom CNN achieved average precision 0.885, recall 0.92, and F1-score 0.90.
• The custom CNN achieved an AUC of 0.92 on the ROC curve.
• Pre-trained CNNs and textural feature-based ANN were evaluated alongside the custom CNN, demonstrating feasibility of GASF-based epilepsy detection.
• Nine-page paper includes 8 figures and 2 tables supporting the results.