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Cui H, Zhong X, Li H, Li C, Dong X, Ji D, He L, Zhou W. A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection. Int J Neural Syst 2024:2450065. [PMID: 39347621 DOI: 10.1142/s0129065724500655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG. The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction of multi-channel EEGs, thereby improving model computational efficiency and real-time performance. Initially, the raw EEG signals undergo Discrete Wavelet Transform (DWT) for signal filtering, and then fed into the DR module for data compression and reshaping while preserving local features. Subsequently, these local features are sent to the EAC module to extract global features and perform categorization. Post-processing involving sliding window averaging, thresholding, and collar techniques is further deployed to reduce the false detection rate (FDR) and improve detection performance. On the CHB-MIT scalp EEG dataset, our method achieves an average sensitivity of 97.57%, accuracy of 98.09%, and specificity of 98.11% at segment-based level, and a sensitivity of 96.81%, along with FDR of 0.27/h, and latency of 17.81 s at the event-based level. On the SH-SDU dataset we collected, our method yielded segment-based sensitivity of 94.51%, specificity of 92.83%, and accuracy of 92.81%, along with event-based sensitivity of 94.11%. The average testing time for 1[Formula: see text]h of multi-channel EEG signals is 1.92[Formula: see text]s. The excellent results and fast computational speed of the CNN-Reformer model demonstrate its potential for efficient seizure detection.
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Affiliation(s)
- Haozhou Cui
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Xiangwen Zhong
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Haotian Li
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Chuanyu Li
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Xingchen Dong
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Dezan Ji
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Landi He
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
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Dong X, Wen Y, Ji D, Yuan S, Liu Z, Shang W, Zhou W. Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model. Int J Neural Syst 2024; 34:2450012. [PMID: 38230571 DOI: 10.1142/s0129065724500126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5-45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.
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Affiliation(s)
- Xingchen Dong
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Yiming Wen
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Dezan Ji
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Shasha Yuan
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Zhen Liu
- Second Hospital of Shandong University, Jinan 250100, P. R. China
| | - Wei Shang
- Second Hospital of Shandong University, Jinan 250100, P. R. China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
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Si X, Yang Z, Zhang X, Sun Y, Jin W, Wang L, Yin S, Ming D. Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN. J Neural Eng 2023; 20. [PMID: 36626831 DOI: 10.1088/1741-2552/acb1d9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 01/10/2023] [Indexed: 01/11/2023]
Abstract
Objective.Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics. However, they still have shortcomings of large memory footprints and slow inference speed.Approach.To solve the above problems of the current study, we propose a novel lightweight convolutional neural network model combining the Convolutional Block Attention Module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.Main results.Our method achieves 83.81% sensitivity (SEN) and 85.4% specificity (SPE) on the SWEC-ETHZ dataset and 86.63% SEN and 92.21% SPE on the TJU-HH dataset. In particular, it takes only 11 ms to infer 10 min iEEG (128 channels), and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.Significance.To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Zhuobin Yang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weipeng Jin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Le Wang
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Shaoya Yin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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Sahani M, Rout SK, Dash PK. FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107639] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Yadav VP, Sharma KK. Variational mode decomposition-based seizure classification using Bayesian regularized shallow neural network. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5128729. [PMID: 32802149 PMCID: PMC7416238 DOI: 10.1155/2020/5128729] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/08/2020] [Indexed: 11/17/2022]
Abstract
The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals. In order to improve the effect of automatic detection, this study proposes a new classification method based on unsupervised multiview clustering results. In addition, considering the high-dimensional characteristics of the original data samples, a deep convolutional neural network (DCNN) is introduced to extract the sample features to obtain deep features. The deep feature reduces the sample dimension and increases the sample separability. The main steps of our proposed novel EEG detection method contain the following three steps: first, a multiview FCM clustering algorithm is introduced, and the training samples are used to train the center and weight of each view. Then, the class center and weight of each view obtained by training are used to calculate the view-weighted membership value of the new prediction sample. Finally, the classification label of the new prediction sample is obtained. Experimental results show that the proposed method can effectively detect seizures.
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Wan X, Fang Z, Wu M, Du Y. Automatic detection of HFOs based on singular value decomposition and improved fuzzy c-means clustering for localization of seizure onset zones. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Geng M, Zhou W, Liu G, Li C, Zhang Y. Epileptic Seizure Detection Based on Stockwell Transform and Bidirectional Long Short-Term Memory. IEEE Trans Neural Syst Rehabil Eng 2020; 28:573-580. [PMID: 31940545 DOI: 10.1109/tnsre.2020.2966290] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automatic seizure detection plays a significant role in monitoring and diagnosis of epilepsy. This paper presents an efficient automatic seizure detection method based on Stockwell transform (S-transform) and bidirectional long short-term memory (BiLSTM) neural networks for intracranial EEG recordings. First, S-transform is applied to raw EEG segments, and the obtained matrix is grouped into time-frequency blocks as the inputs fed into BiLSTM for feature selecting and classification. Afterwards, postprocessing is adopted to improve detection performance, which includes moving average filter, threshold judgment, multichannel fusion, and collar technique. A total of 689 h intracranial EEG recordings from 20 patients are used for evaluation of the proposed system. Segment-based assessment results show that our system achieves a sensitivity of 98.09% and specificity of 98.69%. For the event-based evaluation, a sensitivity of 96.3% and a false detection rate of 0.24/h are yielded. The satisfactory results indicate that this seizure detection approach possess promising potential for clinical practice.
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Sartipi S, Kalbkhani H, Ghasemzadeh P, Shayesteh MG. Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105905] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Liu G, Zhou W, Geng M. Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network. Int J Neural Syst 2019; 30:1950024. [DOI: 10.1142/s0129065719500242] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networks (CNN), is proposed to detect seizure onsets in long-term intracranial EEG recordings. Primarily, raw EEG data is filtered with wavelet decomposition. Then, S-transform is used to obtain a proper time-frequency representation of each EEG segment. After that, a 15-layer deep CNN using dropout and batch normalization serves as a robust feature extractor and classifier. Finally, smoothing and collar technique are applied to the outputs of CNN to improve the detection accuracy and reduce the false detection rate (FDR). The segment-based and event-based evaluation assessments and receiver operating characteristic (ROC) curves are employed for the performance evaluation on a public EEG database containing 21 patients. A segment-based sensitivity of 97.01% and a specificity of 98.12% are yielded. For the event-based assessment, this method achieves a sensitivity of 95.45% with an FDR of 0.36/h.
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Affiliation(s)
- Guoyang Liu
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Minxing Geng
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
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Fukami T, Shimada T, Ishikawa B. Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution. J Neural Eng 2018; 15:036030. [PMID: 29560928 DOI: 10.1088/1741-2552/aab84c] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). APPROACH We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. MAIN RESULTS Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6 ± 36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. SIGNIFICANCE Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.
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Affiliation(s)
- Tadanori Fukami
- Department of Informatics, Faculty of Engineering, Yamagata University, Yonezawa, Yamagata, 992-8510, Japan
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Detection of Epileptic Seizures Using Phase-Amplitude Coupling in Intracranial Electroencephalography. Sci Rep 2016; 6:25422. [PMID: 27147119 PMCID: PMC4857088 DOI: 10.1038/srep25422] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 04/15/2016] [Indexed: 12/04/2022] Open
Abstract
Seizure detection using intracranial electroencephalography (iEEG) contributes to improved treatment of patients with refractory epilepsy. For that purpose, a feature of iEEG to characterize the ictal state with high specificity and sensitivity is necessary. We evaluated the use of phase–amplitude coupling (PAC) of iEEG signals over a period of 24 h to detect the ictal and interictal states. PAC was estimated by using a synchronisation index (SI) for iEEG signals from seven patients with refractory temporal lobe epilepsy. iEEG signals of the ictal state was characterised by a strong PAC between the phase of β and the amplitude of high γ. Furthermore, using SI values, the ictal state was successfully detected with significantly higher accuracy than by using the amplitude of high γ alone. In conclusion, PAC accurately distinguished the ictal state from the interictal state.
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Liu Y, Yu Z, Zeng M, Zhang Y. LLE for submersible plunger pump fault diagnosis via joint wavelet and SVD approach. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.12.054] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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