Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Epileptic Seizure Detection for Multi-channel EEG with Recurrent Convolutional Neural Networks

순환 합성곱 신경망를 이용한 다채널 뇌파 분석의 간질 발작 탐지

  • Yoo, Ji-Hyun (Dept. of Internet Communications, Jangan University)
  • Received : 2018.12.11
  • Accepted : 2018.12.19
  • Published : 2018.12.31
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Abstract

In this paper, we propose recurrent CNN(Convolutional Neural Networks) for detecting seizures among patients using EEG signals. In the proposed method, data were mapped by image to preserve the spectral characteristics of the EEG signal and the position of the electrode. After the spectral preprocessing, we input it into CNN and extracted the spatial and temporal features without wavelet transform. Results from the Children's Hospital of Boston Massachusetts Institute of Technology (CHB-MIT) dataset showed a sensitivity of 90% and a false positive rate (FPR) of 0.85 per hour.

본 논문에서는 뇌파 신호를 이용하여 환자의 경련을 감지하는 순환 CNN (Convolutional Neural Networks)을 제안한다. 제안 된 방법은 뇌파 신호의 스펙트럼 특성과 전극의 위치를 보존하기 위해 영상으로 데이터를 매핑하여 처리하였다. 스펙트럼 전처리 과정을 거친 후 CNN에 입력하고 공간 및 시간 특성을 웨이블릿 변환(wavelet transform)없이 추출하여 발작을 검출하였다. 여기에 사용된 보스턴 매사추세츠 공과 대학 (Boston Massachusetts Institute of Technology, CHB-MIT) 아동 병원의 데이터셋 결과는 시간당 0.85의 민감도와 90 %의 위양성 비율 (FPR)을 보였다.

Keywords

JGGJB@_2018_v22n4_1175_f0001.png 이미지

Fig. 1. Pre-processing process using EEG signals of CHB-MIT dataset. 그림 1. CHB-MIT 데이터셋의 EGG 시그널을 사용한 전처리 과정

JGGJB@_2018_v22n4_1175_f0002.png 이미지

Fig. 2. Proposed CNN Architecture. 그림 2. 제안한 CNN 구조

JGGJB@_2018_v22n4_1175_f0003.png 이미지

Fig. 3. Proposed Classification Process. 그림 3. 제안한 분류 프로세스

Table 1. Result for CHB-MIT Scalp EEG dataset. 표 1. CHB-MIT Scalp EEG 데이터셋 결과

JGGJB@_2018_v22n4_1175_t0001.png 이미지

Table 2. Performance of seizure detection method. 표 2. 발작 검출 성능

JGGJB@_2018_v22n4_1175_t0002.png 이미지

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