Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM

被引:22
作者
Mahmud, Tanvir [1 ]
Khan, Ishtiaque Ahmed [1 ]
Mahmud, Talha Ibn [1 ]
Fattah, Shaikh Anowarul [1 ]
Zhu, Wei-Ping [2 ]
Ahmad, M. Omair [2 ]
机构
[1] Bangladesh Univ Engn & Technol, Dept Elect & Elect Engn EEE, Dhaka 1205, Bangladesh
[2] Concordia Univ, Dept Elect & Commun Engn ECE, Montreal, PQ H3G 1M8, Canada
关键词
Electroencephalography; Feature extraction; Sleep apnea; Neural networks; Training; Convolution; Frequency conversion; variational mode decomposition; computer-aided diagnosis; neural network; EEG signal; ASSOCIATION; RISK;
D O I
10.1109/ACCESS.2021.3097090
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Sleep apnea, a severe sleep disorder, is a clinically complicated disease that requires timely diagnosis for proper treatment. In this paper, an automated deep learning-based approach is proposed for the detection of sleep apnea frames from electroencephalogram (EEG) signals. Unlike conventional methods of direct feature extraction from EEG signals, the variational mode decomposition (VMD) algorithm is utilized in the proposed method to decompose the EEG signals into a number of modes. Use of such decomposed EEG signals for feature extraction offers efficient processing of the variations introduced in the frequency spectrum during apnea events irrespective of particular patients. Afterward, a fully convolutional neural network (FCNN) is proposed to separately extract the temporal features from each VMD mode in parallel while maintaining their temporal dependencies. The FCNN block utilizes causal dilated convolutions with increasing dilation rates along with multiple kernel operations in convolutions. Subsequently, for further exploration of the inter-modal temporal variations, these extracted features from different EEG-modes are jointly optimized with a stack of bi-directional long short term memory (LSTM) layers. Hence, the trained and optimized network is capable of generating predictions of apnea frames during the evaluation phase. Contrary to other studies, this study is carried out in a subject independent manner where separate subjects are considered for training and testing. Additionally, a semi-supervised approach is explored where for facilitating better classification performance on a subject's frames, a small portion of the patient's data is included in training to leverage insight regarding the possible environmental variations. Extensive experimentations on three publicly available datasets provide average accuracy of 93.22%, 93.25% and 89.41% in the subject-independent cross-validation scheme.
引用
收藏
页码:102355 / 102367
页数:13
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