{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T21:50:10Z","timestamp":1741384210572,"version":"3.38.0"},"reference-count":46,"publisher":"Tech Science Press","issue":"1","license":[{"start":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T00:00:00Z","timestamp":1729382400000},"content-version":"vor","delay-in-days":293,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.056016","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T20:40:12Z","timestamp":1727296812000},"page":"1177-1196","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Stroke Electroencephalogram Data Synthesizing through Progressive Efficient Self-Attention Generative Adversarial Network"],"prefix":"10.32604","volume":"81","author":[{"given":"Suzhe","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xueying","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fenglian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zelin","family":"Wu","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"412","DOI":"10.5853\/jos.2020.01928","article-title":"National trends in clinical outcomes of endovascular therapy for ischemic stroke in South Korea between 2008 and 2016","volume":"22","author":"Seo","year":"Sep. 2020","journal-title":"J. Stroke"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.5853\/jos.2016.01935","article-title":"Spontaneous intracerebral hemorrhage: Management","volume":"19","author":"Kim","year":"Jan. 2017","journal-title":"J. Stroke"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1038\/s41598-021-84441-0","article-title":"Pre-hospital triage of suspected acute stroke patients in a mobile stroke unit in the rural alberta","volume":"11","author":"Kate","year":"Mar. 2021","journal-title":"Sci. Rep."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1177\/1550059419875916","article-title":"Quantitative and qualitative EEG as a prediction tool for outcome and complications in acute stroke patients","volume":"51","author":"Doerrfuss","year":"Sep. 2019","journal-title":"Clin. EEG Neurosci."},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1186\/s12883-022-02835-7","article-title":"Perioperative stroke during carotid endarterectomy: Benefits of multimodal neuromonitoring-A case report","volume":"22","author":"Michels","year":"Aug. 2022","journal-title":"BMC Neurol."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1007\/s13042-022-01718-0","article-title":"Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals","volume":"14","author":"Barua","year":"2023","journal-title":"Int. J. Mach. Learn. Cyb."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s12652-021-03324-4","article-title":"A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning","volume":"14","author":"Subasi","year":"2023","journal-title":"J. Amb. Intel. Hum. Comp."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.54097\/12cjyf16","article-title":"Detection and classification of ADHD using deep learning based on EEG signals","volume":"91","author":"Liu","year":"2024","journal-title":"Highlig. Sci., Eng. Technol."},{"key":"ref9","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TNSRE.2022.3145515","article-title":"BWGAN-GP: An EEG data generation method for class imbalance problem in RSVP tasks","volume":"30","author":"Xu","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2021.765525","article-title":"Data augmentation for deep neural networks model in EEG classification task: A review","volume":"15","author":"He","year":"Dec. 2021","journal-title":"Front. Hum. Neurosci."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"Oct. 2020","journal-title":"Commun. ACM"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-023-01169-w","article-title":"Generative adversarial networks in EEG analysis: An overview","volume":"20","author":"Habashi","year":"2023","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref13","unstructured":"Z. Zhang, S. Zhong, and Y. Liu, \u201cBeyond mimicking under-represented emotions: Deep data augmentation with emotional subspace constraints for EEG-based emotion recognition,\u201d presented at AAAI Conf. Artif. Intell., Vancouver, BC, Canada, Feb. 22\u201325, 2024, pp. 22\u201325."},{"key":"ref14","first-page":"22","article-title":"An epileptic EEG detection method based on data augmentation and lightweight neural network","volume":"12","author":"Wang","year":"Aug. 2023","journal-title":"IEEE J. Transl. Eng. He."},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.3390\/brainsci14040367","article-title":"Electroencephalographic signal data augmentation based on improved generative adversarial network","volume":"14","author":"Du","year":"Apr. 2024","journal-title":"Brain Sci."},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"S. M. Abdelfattah, G. M. Abdelrahman, and M. Wang, \u201cAugmenting the size of EEG datasets using generative adversarial networks,\u201d presented at the 2018 Int. Jt. Conf. Neural Networks, Rio de janeiro, Brazil, Jul. 08\u201313, 2018.","DOI":"10.1109\/IJCNN.2018.8489727"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TBME.2020.3042574","article-title":"Epilepsygan: Synthetic epileptic brain activities with privacy preservation","volume":"68","author":"Pascual","year":"Dec. 2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"R. Kadri, M. Tmar, B. Bouaziz, and F. Gargouri, \u201cAlzheimer\u2019s disease detection using deep ECA-ResNet101 network with DCGAN,\u201d presented at the 2021 Int. Jt. Conf. Hybrid Intell. Syst., Ottawa, ON, Canada, Dec. 14\u201316, 2021, pp. 14\u201316.","DOI":"10.1007\/978-3-030-96305-7_35"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"F. Fahimi, Z. Zhang, W. B. Goh, K. K. Ang, and C. Guan, \u201cTowards EEG generation using GANs for BCI applications,\u201d presented at the 2019 Int. Jt. Conf. Biomed. Health. Inf., Chicago, IL, USA, May 19\u201322, 2019.","DOI":"10.1109\/BHI.2019.8834503"},{"key":"ref20","first-page":"1","article-title":"EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN","volume":"8","author":"Zhang","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"G. Bouallegue and R. Djemal, \u201cEEG data augmentation using Wasserstein GAN,\u201d presented at the 2020 Int. Conf. Sci. Tech. Autom., Monastir, Tunisia, Dec. 20\u201322, 2020.","DOI":"10.1109\/STA50679.2020.9329330"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2020.00015","article-title":"EEG signal reconstruction using a generative adversarial network with wasserstein distance and temporal-spatial-frequency loss","volume":"14","author":"Luo","year":"Apr. 2020","journal-title":"Front. Neuroinform."},{"key":"ref23","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1109\/TNSRE.2020.3006180","article-title":"Modeling EEG data distribution with a Wasserstein generative adversarial network to predict RSVP events","volume":"28","author":"Panwar","year":"Aug. 2020","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1007\/s10489-019-01624-z","article-title":"Cost-sensitive hierarchical classification for imbalance classes","volume":"50","author":"Zheng","year":"Mar. 2020","journal-title":"Appl. Intell."},{"key":"ref25","unstructured":"V. Kushwaha and G. C. Nandi, \u201cStudy of prevention of mode collapse in generative adversarial network (GAN),\u201d presented at the 2020 Int. Conf. Inf. Commun. Tech., Changchun, China, May 24\u201326, 2020."},{"key":"ref26","doi-asserted-by":"crossref","first-page":"119668","DOI":"10.1109\/ACCESS.2019.2936709","article-title":"Improving human pose estimation with self-attention generative adversarial networks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.neucom.2020.10.092","article-title":"SA-CapsGAN: Using capsule networks with embedded self-attention for generative adversarial networks","volume":"423","author":"Sun","year":"Jan. 2021","journal-title":"Neurocomputing"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"4951","DOI":"10.1109\/TNSM.2023.3260039","article-title":"Unsupervised GAN-based intrusion detection system using temporal convolutional networks and self-attention","volume":"20","author":"de Araujo-Filho","year":"Dec. 2023","journal-title":"IEEE Trans. Netw. Serv."},{"key":"ref29","first-page":"1","article-title":"Plane-wave image reconstruction via generative adversarial network and attention mechanism","volume":"70","author":"Tang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105828","article-title":"Towards precision sleep medicine: Self-attention GAN as an innovative data augmentation technique for developing personalized automatic sleep scoring classification","volume":"148","author":"Kuo","year":"Sep. 2022","journal-title":"Comput. Biol. Med."},{"key":"ref31","first-page":"1","article-title":"A GAN guided parallel CNN and transformer network for EEG denoising","volume":"5","author":"Yin","year":"2024","journal-title":"IEEE J. Biomed. Health."},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119060","article-title":"Distilling and transferring knowledge via cGAN-generated samples for image classification and regression","volume":"213","author":"Ding","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"H. Zhao, J. Jia, and V. Koltun, \u201cExploring self-attention for image recognition,\u201d presented at the 2020 Proc. Int. Conf. Comput. Vis. Pattern Recog., Seattle, WA, USA, Jun. 14\u201319, 2020.","DOI":"10.1109\/CVPR42600.2020.01009"},{"key":"ref34","unstructured":"A. Vaswani et al., \u201cAttention is all you need,\u201d presented at Adv. Neural Inf. Process. Syst., Long Beach, CA, USA, Dec. 4\u20139, 2017."},{"key":"ref35","article-title":"Directed message passing based on attention for prediction of molecular properties","volume":"229","author":"Chen","year":"Oct. 2023","journal-title":"Comp. Mater. Sci."},{"key":"ref36","unstructured":"K. Zhang, I. W. Tsang, and J. T. Kwok, \u201cImproved Nystr\u00f6m low-rank approximation and error analysis,\u201d presented at the 2020 Proc. Int. Conf. Mach. Learn., Vienna, Austria, Jul. 12\u201318, 2020."},{"key":"ref37","first-page":"7264","article-title":"Convolutional neural networks on graphs with Chebyshev approximation, revisited","volume":"35","author":"He","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"J. T. Barron, \u201cA general and adaptive robust loss function,\u201d presented at the 2019 Proc. Int. Conf. Comput. Vis. Pattern Recog., Long Beach, CA, USA, Jun. 15\u201320, 2019.","DOI":"10.1109\/CVPR.2019.00446"},{"key":"ref39","unstructured":"S. Cao, \u201cChoose a transformer: Fourier or Galerkin,\u201d presented at the 2021 Proc. Int. Conf. Adv. Neural Inf. Process. Syst., Dec. 8\u201314, 2021."},{"key":"ref40","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/TNSRE.2021.3125023","article-title":"A generative model to synthesize EEG data for epileptic seizure prediction","volume":"29","author":"Rasheed","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"key":"ref41","doi-asserted-by":"crossref","first-page":"98716","DOI":"10.1109\/ACCESS.2020.2997001","article-title":"DCGAN-based data augmentation for tomato leaf disease identification","volume":"8","author":"Wu","year":"2022","journal-title":"IEEE Access"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"18450","DOI":"10.1109\/ACCESS.2019.2896409","article-title":"WGAN-based synthetic minority over-sampling technique: Improving semantic fine-grained classification for lung nodules in CT images","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"3289","DOI":"10.1109\/JSYST.2019.2935457","article-title":"E-WACGAN: Enhanced generative model of signaling data based on WGAN-GP and ACGAN","volume":"14","author":"Jin","year":"Sep. 2020","journal-title":"IEEE Syst. J."},{"key":"ref44","article-title":"Generative adversarial networks in medical image augmentation: A review","volume":"6","author":"Chen","year":"Nov. 2023","journal-title":"Comput. Biol. Med."},{"key":"ref45","article-title":"A novel loss function for neural network models exploring stock realized volatility using wasserstein distance","volume":"10","author":"Gobato","year":"May 2024","journal-title":"Dec. Anal. J."},{"key":"ref46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3665138","article-title":"Efficient automation of neural network design: A survey on differentiable neural architecture search","volume":"56","author":"Heuillet","year":"Jun. 2024","journal-title":"ACM Comput. Surv."}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-81-1\/TSP_CMC_56016\/TSP_CMC_56016.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T03:00:21Z","timestamp":1741316421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n1\/58357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.056016","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-07-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-09","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-15","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}