순환 합성곱 신경망를 이용한다채널 뇌파 분석의 간질 발작 탐지
Epileptic Seizure Detection for Multi-channel EEG with Recurrent Convolutional Neural Networks
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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 발작을검출하였다. 여기에 사용된 보스턴 매사추세츠 공과 대학 (Boston Massachusetts Institute of Technology, CHB-MIT) 아동병원의 데이터셋 결과는 시간당 0.85의 민감도와 90 %의 위양성 비율 (FPR)을 보였다.
Abstract
In this paper, we propose recurrent CNN(Convolutional Neural Networks) for detecting seizures among patients usingEEG signals. In the proposed method, data were mapped by image to preserve the spectral characteristics of the EEGsignal and the position of the electrode. After the spectral preprocessing, we input it into CNN and extracted the spatialand temporal features without wavelet transform. Results from the Children's Hospital of Boston Massachusetts Institute of Technology (CHB-MIT) dataset showed asensitivity of 90% and a false positive rate (FPR) of 0.85 per hour.
참고문헌 (11)
- Dong Wook Kim, "Clinical characteristics of patients with treated epilepsy in Korea: A nationwide epidemiologic study", Epilepsia, 55(1), pp.67~75 (2014), Wiley[DOI]
- Ralph Meier, "Detecting Epileptic Seizures in Long-term Human EEG: A New Approach to Automatic Online and Real-Time Detection and Classification of Polymorphic Seizure Patterns", Journal of Clinical Neurophysiology, 25(3), pp.119~131 (2008), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Alexander M. Chan, "Automated seizure onset detection for accurate onset time determination in intracranial EEG", Clinical Neurophysiology, 119(12), pp.2687~2696 (2008), Elsevier BV[DOI]
- A. Aarabi, "A fuzzy rule-based system for epileptic seizure detection in intracranial EEG", Clinical Neurophysiology, 120(9), pp.1648~1657 (2009), Elsevier BV[DOI]
- U. Rajendra Acharya, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals", Computers in Biology and Medicine, 100, pp.270~278 (2018), Elsevier BV[DOI]
- L. Vidyaratne, "Deep recurrent neural network for seizure detection. In Neural Networks", International Joint Conference, pp.1202~1207 (2016), IEEE
- M. Golmohammadi, "Gated recurrent networks for seizure detection", Signal Processing in Medicine and Biology Symposium (SPMB), pp.1~5 (2017), IEEE
- P. Bashivan, "Learning Representations from EEG with Deep Recurrent Convolutional Neural Networks", ~ (2016)
- P. Thodoroff, "Learning robust features using deep learning for automatic seizure detection", Machine Learning for Healthcare Conference, pp.178~190 (2016)
- R. K. Maddula, "Deep recurrent convolutional neural networks for classifying P300 BCI signals", Proceedings of the Graz BCI Conference (2017)
- D. Tran, "Learning spatiotemporal features with 3d convolutional networks", Computer Vision (ICCV), 2015 IEEE International Conference on, pp.4489~4497 (2015)