{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:36:51Z","timestamp":1771843011272,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["81871394, 61672064"],"award-info":[{"award-number":["81871394, 61672064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Laboratory of Advanced Information Networks","award":["040000546618017"],"award-info":[{"award-number":["040000546618017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, pervasive sensing technologies have been widely applied to comprehensive patient monitoring in order to improve clinical treatment. Various types of biomedical signals collected by different sensing channels provide different aspects of patient health information. However, due to the uncertainty and variability in clinical observation, not all the channels are relevant and important to the target task. Thus, in order to extract informative representations from multi-channel biosignals, channel awareness has become a key enabler for deep learning in biosignal processing and has attracted increasing research interest in health informatics. Towards this end, we propose FusionAtt\u2014a deep fusional attention network that can learn channel-aware representations of multi-channel biosignals, while preserving complex correlations among all the channels. FusionAtt is able to dynamically quantify the importance of each biomedical channel, and relies on more informative ones to enhance feature representation in an end-to-end manner. We empirically evaluated FusionAtt in two clinical tasks: multi-channel seizure detection and multivariate sleep stage classification. Experimental results showed that FusionAtt consistently outperformed the state-of-the-art models in four different evaluation measurements, demonstrating the effectiveness of the proposed fusional attention mechanism.<\/jats:p>","DOI":"10.3390\/s19112429","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T11:18:09Z","timestamp":1559042289000},"page":"2429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals"],"prefix":"10.3390","volume":"19","author":[{"given":"Ye","family":"Yuan","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Kebin","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/JPROC.2015.2501978","article-title":"Machine learning and decision support in critical care","volume":"104","author":"Johnson","year":"2016","journal-title":"Proc. IEEE Inst. Electr. Electr. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Acar, E., Levin-Schwartz, Y., Calhoun, V.D., and Adali, T. (2017, January 28\u201331). Tensor-based fusion of EEG and FMRI to understand neurological changes in schizophrenia. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA.","DOI":"10.1109\/ISCAS.2017.8050303"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, X., Jia, X., Xun, G., and Zhang, A. (November, January 29). Improving eeg feature learning via synchronized facial video. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7363831"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xun, G., Jia, K., and Zhang, A. (2017, January 20\u201323). A multi-view deep learning method for epileptic seizure detection using short-time fourier transform. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Boston, MA, USA.","DOI":"10.1145\/3107411.3107419"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"Karlsson","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Supratak, A., Wu, C., Dong, H., Sun, K., and Guo, Y. (2016). Survey on feature extraction and applications of biosignals. Machine Learning for Health Informatics, Springer.","DOI":"10.1007\/978-3-319-50478-0_8"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, K., Li, X., Zhang, Y., and Zhang, A. (2013, January 18\u201321). Affective state recognition from EEG with deep belief networks. Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China.","DOI":"10.1109\/BIBM.2013.6732507"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jia, X., Li, K., Li, X., and Zhang, A. (2014, January 10\u201312). A novel semi-supervised deep learning framework for affective state recognition on eeg signals. Proceedings of the 2014 IEEE International Conference on Bioinformatics And Bioengineering, Boca Raton, FL, USA.","DOI":"10.1109\/BIBE.2014.26"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1177\/0142331215587568","article-title":"Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers","volume":"38","author":"Zhang","year":"2016","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_11","first-page":"5","article-title":"Sleep stage classification using unsupervised feature learning","volume":"2012","author":"Karlsson","year":"2012","journal-title":"Adv. Artif. Neural Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xun, G., Jia, X., and Zhang, A. (2016). Detecting epileptic seizures with electroencephalogram via a context-learning model. BMC Med. Inf. Dec. Mak., 16.","DOI":"10.1186\/s12911-016-0310-7"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xun, G., Jia, K., and Zhang, A. (2017, January 13\u201316). A novel wavelet-based model for eeg epileptic seizure detection using multi-context learning. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA.","DOI":"10.1109\/BIBM.2017.8217737"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ha, S., and Choi, S. (2016, January 24\u201329). Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727224"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yao, S., Hu, S., Zhao, Y., Zhang, A., and Abdelzaher, T. (2017, January 3\u20137). Deepsense: A unified deep learning framework for time-series mobile sensing data processing. Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, Perth, Australia.","DOI":"10.1145\/3038912.3052577"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., and Sun, J. (2017, January 13\u201317). GRAM: graph-based attention model for healthcare representation learning. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098126"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., and Gao, J. (2017, January 13\u201317). Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098088"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xun, G., Ma, F., Suo, Q., Xue, H., Jia, K., and Zhang, A. (2018, January 4\u20137). A novel channel-aware attention framework for multi-channel eeg seizure detection via multi-view deep learning. Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2018.8333405"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: Recent progress and new challenges","volume":"38","author":"Zhao","year":"2017","journal-title":"Inf. Fus."},{"key":"ref_20","unstructured":"Shoeb, A.H. (2009). Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xun, G., Suo, Q., Jia, K., and Zhang, A. (2017, January 18\u201321). Wave2vec: Learning deep representations for biosignals. Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.155"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2011). Principal Component Analysis, Springer.","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/11\/2429\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:00Z","timestamp":1760187240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/11\/2429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,28]]},"references-count":25,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19112429"],"URL":"https:\/\/doi.org\/10.3390\/s19112429","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,28]]}}}