期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 26, 期 10, 页码 4903-4912出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2022.3159531
关键词
Electroencephalography; Feature extraction; Pipelines; Convolutional neural networks; Image segmentation; Continuous wavelet transforms; Image synthesis; Convolution neural network; continuous wavelet transform; electroencephalogram; hilbert vibration decomposition; long short-term memory; seizure types
类别
资金
- North East Centre for Biological Sciences and Healthcare Engineering (NECBH) Twinning Outreach Programme [NECBH/2019-20/118]
- Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India [BT/COE/34/SP28408/2018]
This study proposes a method for classifying seizure types based on electroencephalogram (EEG) signals. By decomposing the signals into subcomponents and converting them into 2D images for deep learning inputs, deep features are effectively extracted. By combining convolutional neural networks and long short-term memory, the classification of different types of seizures is achieved with high accuracy.
Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for the diagnosis and prognosis of epileptic patients. The minuscule changes reflected in EEG signals among different seizure types make such tasks more challenging. Therefore, in this work, underlying features in EEG have been explored by decomposing signals into multiple subcomponents which have been further used to generate 2D input images for deep learning (DL) pipeline. The Hilbert vibration decomposition (HVD) has been employed for decomposing the EEG signals by preserving phase information. Next, 2D images have been generated considering the first three subcomponents having high energy by involving continuous wavelet transform and converting them into 2D images for DL inputs. For classification, a hybrid DL pipeline has been constructed by combining the convolution neural network (CNN) followed by long short-term memory (LSTM) for efficient extraction of spatial and time sequence information. Experimental validation has been conducted by classifying five types of seizures and seizure-free, collected from the Temple University EEG dataset (TUH v1.5.2). The proposed method has achieved the highest classification accuracy up to 99% along with an F1-score of 99%. Further analysis shows that the HVD-based decomposition and hybrid DL model can efficiently extract in-depth features while classifying different types of seizures. In a comparative study, the proposed idea demonstrates its superiority by displaying the uppermost performance.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据