{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T01:59:35Z","timestamp":1775008775792,"version":"3.50.1"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3389\/fncom.2024.1358780","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T04:24:31Z","timestamp":1706156671000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Positional multi-length and mutual-attention network for epileptic seizure classification"],"prefix":"10.3389","volume":"18","author":[{"given":"Guokai","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Aiming","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jihao","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Jianqing","family":"Chen","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"103654","DOI":"10.1016\/j.jisa.2023.103654","article-title":"An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection","volume":"80","author":"Ahmad","year":"2024","journal-title":"J. Inform. Commun. Technol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3390\/diagnostics12010074","article-title":"Determinant of covariance matrix model coupled with adaboost classification algorithm for EEG seizure detection","volume":"12","author":"Al-Hadeethi","year":"2021","journal-title":"Diagnostics"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-020-05666-0","article-title":"Selection of optimal wavelet features for epileptic EEG signal classification with LSTM","volume":"2021","author":"Aliyu","year":"2021","journal-title":"Neural Comp. Appl"},{"key":"B4","doi-asserted-by":"publisher","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","article-title":"Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state","author":"Andrzejak","year":"2001","journal-title":"Phys. Rev"},{"key":"B5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.compbiomed.2016.10.019","article-title":"Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition","volume":"79","author":"Chai","year":"2016","journal-title":"Comp. Biol. Med"},{"key":"B6","first-page":"77","article-title":"Dilated recurrent neural networks","volume":"30","author":"Chang","year":"2017","journal-title":"Adv. Neural Inf. Process Syst"},{"key":"B7","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/TCBB.2023.3247433","article-title":"Robust deep learning framework based on spectrograms for heart sound classification","volume":"22","author":"Chen","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.1109\/TII.2021.3088465","article-title":"Compressed sensing framework for heart sound acquisition in internet of medical things","volume":"18","author":"Chen","year":"2021","journal-title":"IEEE Trans. Indust. Informat"},{"key":"B9","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/ICIINFS.2018.8721426","article-title":"Detection of epileptic seizure event in EEG signals using variational mode decomposition and mode spectral entropy","volume-title":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","author":"Das","year":"2018"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1109\/TNSRE.2018.2850308","article-title":"Transductive joint-knowledge-transfer TSK FS for recognition of epileptic EEG signals","volume":"26","author":"Deng","year":"2018","journal-title":"IEEE Trans. Neural Syst.d Rehabilit. Eng"},{"key":"B11","doi-asserted-by":"publisher","first-page":"609","DOI":"10.3390\/e21060609","article-title":"Recognition of emotional states using multiscale information analysis of high frequency EEG oscillations","volume":"21","author":"Gao","year":"2019","journal-title":"Entropy"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3241056","article-title":"Applying deep learning for epilepsy seizure detection and brain mapping visualization","volume":"15","author":"Hossain","year":"2019","journal-title":"ACM Trans. Multimedia Comp. Commun. Applicat. (TOMM)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"107941","DOI":"10.1016\/j.apacoust.2021.107941","article-title":"Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks","volume":"177","author":"Hussain","year":"2021","journal-title":"Appl. Acoust"},{"key":"B14","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"International Conference on Machine Learning","author":"Ioffe","year":"2015"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1109\/TBME.2016.2633391","article-title":"Automatic detection and classification of high-frequency oscillations in depth-EEG signals","volume":"64","author":"Jrad","year":"2016","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"B16","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.ins.2014.06.028","article-title":"Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes","volume":"294","author":"Kasabov","year":"2015","journal-title":"Inform. Sci"},{"key":"B17","doi-asserted-by":"publisher","first-page":"8928021","DOI":"10.1155\/2022\/8928021","article-title":"On the use of wavelet domain and machine learning for the analysis of epileptic seizure detection from EEG signals","volume":"2022","author":"Kavitha","year":"2022","journal-title":"J. Healthc Eng"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1109\/TBME.2005.851521","article-title":"Spatio-spectral filters for improving the classification of single trial EEG","volume":"52","author":"Lemm","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B19","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1109\/TNSRE.2020.2973434","article-title":"Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network","volume":"28","author":"Li","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng"},{"key":"B20","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3389\/fnsys.2020.00043","article-title":"EEG-based Emotion Classification Using Deep Neural Network and Sparse Autoencoder","volume":"14","author":"Liu","year":"2020","journal-title":"Front. Syst. Neurosci"},{"key":"B21","doi-asserted-by":"publisher","first-page":"106071","DOI":"10.1016\/j.asoc.2020.106071","article-title":"Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application","volume":"89","author":"Liu","year":"2020","journal-title":"Appl. Soft Comp"},{"key":"B22","doi-asserted-by":"publisher","first-page":"3170540","DOI":"10.1109\/TAC.2022.3170540","article-title":"Remote estimation for energy harvesting systems under multiplicative noises: a binary encoding scheme with probabilistic bit flips","volume":"68","author":"Liu","year":"2022","journal-title":"IEEE Trans. Automatic Control"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1109\/TNSRE.2023.3257306","article-title":"Revised tunable q-factor wavelet transform for EEG-based epileptic seizure detection","volume":"31","author":"Liu","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng"},{"key":"B24","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1109\/TBME.2014.2345458","article-title":"Simultaneously optimizing spatial spectral features based on mutual information for EEG classification","volume":"62","author":"Meng","year":"2014","journal-title":"Trans. Biomed. Eng"},{"key":"B25","doi-asserted-by":"publisher","first-page":"3336935","DOI":"10.1109\/JBHI.2023.3336935","article-title":"SMARTSeiz: deep learning with attention mechanism for accurate seizure recognition in iot healthcare devices","volume":"6","author":"Patro","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"B26","doi-asserted-by":"publisher","first-page":"587","DOI":"10.32890\/jict2023.22.4.3","article-title":"Modified gated recurrent unit approach for epileptic electroencephalography classification","volume":"22","author":"Prakash","year":"2023","journal-title":"J. Inform. Commun. Technol"},{"key":"B27","doi-asserted-by":"publisher","first-page":"3070","DOI":"10.1109\/TNNLS.2015.2402694","article-title":"A novel algorithm for spatio-temporal filtering and classification of single-trial EEG","volume":"26","author":"Qi","year":"2015","journal-title":"IEEE Trans. Neural Networks Learning Syst"},{"key":"B28","doi-asserted-by":"publisher","first-page":"104652","DOI":"10.1016\/j.bspc.2023.104652","article-title":"difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal","volume":"83","author":"Qiu","year":"2023","journal-title":"Biomed. Signal Proc. Control"},{"key":"B29","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/TNSRE.2018.2864306","article-title":"Denoising sparse autoencoder-based ictal EEG classification. IEEE Trans","volume":"26","author":"Qiu","year":"2018","journal-title":"Neural Syst. Rehabilit. Eng"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12652-021-03676-x","article-title":"Detecting epilepsy in EEG signals using synchro-extracting-transform (SET) supported classification technique","volume":"2022","author":"Rajinikanth","year":"2022","journal-title":"J. Ambient Intellig. Human. Comp"},{"key":"B31","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/TNSRE.2015.2441835","article-title":"EMD-based temporal and spectral features for the classification of EEG signals using supervised learning","volume":"24","author":"Riaz","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1109\/TENSYMP50017.2020.9230731","article-title":"Efficient approach to detect epileptic seizure using machine learning models for modern healthcare system","volume":"2020","author":"Rohan","year":"2020","journal-title":"IEEE"},{"key":"B33","doi-asserted-by":"publisher","first-page":"102723","DOI":"10.1016\/j.bspc.2021.102723","article-title":"Epileptic seizure detection using novel multilayer LSTM discriminant network and dynamic mode Koopman decomposition","volume":"68","author":"Saichand","year":"2021","journal-title":"Biomed. Signal Proc. Control"},{"key":"B34","article-title":"Bidirectional dilated LSTM with attention for fine-grained emotion classification in tweets","volume-title":"Proceedings of the AAAI-20 Workshop on Affective Content Analysis","author":"Schoene","year":"2020"},{"key":"B35","doi-asserted-by":"publisher","first-page":"103417","DOI":"10.1016\/j.bspc.2021.103417","article-title":"Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies","volume":"73","author":"Shoeibi","year":"2022","journal-title":"Biomed. Signal Proc. Control"},{"key":"B36","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1049\/iet-smt.2018.5358","article-title":"Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure","volume":"13","author":"Siuly","year":"2019","journal-title":"IET Sci. Measur. Technol"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-019-0606-8","article-title":"Spectral information of EEG signals with respect to epilepsy classification","volume":"2019","author":"Tsipouras","year":"2019","journal-title":"EURASIP"},{"key":"B38","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s13246-019-00794-x","article-title":"novel local senary pattern based epilepsy diagnosis system using EEG signals","volume":"42","author":"Tuncer","year":"2019","journal-title":"Aust. Phys. Eng. Sci. Med"},{"key":"B39","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/brainsci9050115","article-title":"Epilepsy detection by using scalogram based convolutional neural network from EEG signals","volume":"9","author":"T\u00fcrk","year":"2019","journal-title":"Brain Sci"},{"key":"B40","doi-asserted-by":"publisher","first-page":"101943","DOI":"10.1016\/j.jocs.2023.101943","article-title":"Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning","volume":"67","author":"Varli","year":"2023","journal-title":"J. Comp. Sci"},{"key":"B41","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B42","doi-asserted-by":"publisher","first-page":"3252569","DOI":"10.1109\/TNNLS.2023.3252569","article-title":"SSGCNet: a sparse spectra graph convolutional network for epileptic EEG signal classification","volume":"16","author":"Wang","year":"2023","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst"},{"key":"B43","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TNSRE.2023.3235390","article-title":"C 2 SP-Net: joint compression and classification network for epilepsy seizure prediction","volume":"31","author":"Wu","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng"},{"key":"B44","article-title":"Epileptic seizure recognition","author":"Wu","year":"2017","journal-title":"UCI Machine Learning Repository"},{"key":"B45","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1109\/TNSRE.2022.3166181","article-title":"An attention-based wavelet convolution neural network for epilepsy EEG classification","volume":"30","author":"Xin","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng"},{"key":"B46","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/JBHI.2018.2871678","article-title":"A multi-view deep learning framework for EEG seizure detection","volume":"23","author":"Yuan","year":"","journal-title":"IEEE J. Biomed. Health Informat"},{"key":"B47","first-page":"206","article-title":"A novel channel-aware attention framework for multi-channel eeg seizure detection via multi-view deep learning","volume-title":"2018 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)","author":"Yuan","year":""},{"key":"B48","doi-asserted-by":"publisher","first-page":"870","DOI":"10.3389\/fnins.2020.00870","article-title":"MNL-network: a multi-scale non-local network for epilepsy detection from EEG signals","volume":"14","author":"Zhang","year":"2020","journal-title":"Front. Neurosci"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/TNSRE.2016.2611601","article-title":"based features for the automatic seizure detection of EEG signals using SVM","volume":"25","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabilitat. Eng."},{"key":"B50","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Mental Dev"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2024.1358780\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T04:24:36Z","timestamp":1706156676000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2024.1358780\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"references-count":50,"alternative-id":["10.3389\/fncom.2024.1358780"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2024.1358780","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]},"article-number":"1358780"}}