{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:17:23Z","timestamp":1790831843109,"version":"4.1.0"},"reference-count":76,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>A debilitating and poorly understood symptom of Parkinson's disease (PD) is freezing of gait (FoG), which increases the risk of falling. Clinical evaluations of FoG, relying on patients\u2019 subjective reports and manual examinations by specialists, are unreliable, and most detection methods are influenced by subject\u2010specific factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>To address this, we developed a novel algorithm for detecting FoG events based on movement signals. To enhance efficiency, we propose a novel architecture integrating a bottleneck attention module into a standard bidirectional long short\u2010term memory network (BiLSTM). This architecture, adaptable to a convolution bottleneck attention\u2013BiLSTM (CBA\u2010BiLSTM), classifies signals using data from ankle, leg, and trunk sensors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Finding<\/jats:title>\n                    <jats:p>Given three movement directions from three locations, we reduce computational complexity in two phases: selecting optimal channels through ensemble learning followed by feature reduction using attention mapping. In FoG event detection tests, performance improved significantly compared to control groups and existing methods, achieving 99.88% accuracy with only two channels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The reduced computational complexity enables real\u2010time monitoring. Our approach demonstrates substantial improvements in classification results compared to traditional deep learning methods.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.70206","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T19:14:12Z","timestamp":1735672452000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep Learning\u2013Based Prediction of Freezing of Gait in Parkinson's Disease With the Ensemble Channel Selection Approach"],"prefix":"10.1002","volume":"15","author":[{"given":"Sara","family":"Abbasi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Islamic Azad University of Mashhad  Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-6626","authenticated-orcid":false,"given":"Khosro","family":"Rezaee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Meybod University  Meybod Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517\u2010015\u20101395\u20103"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21196446"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.036"},{"key":"e_1_2_13_5_1","doi-asserted-by":"crossref","unstructured":"Assam R. andT.Seidl.2014. \u201cPrediction of Freezing of Gait From Parkinson's Disease Movement Time Series Using Conditional Random Fields.\u201d InProceedings of the Third ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health 11\u201320.New York:ACM.https:\/\/doi.org\/10.1145\/2676629.2676630.","DOI":"10.1145\/2676629.2676630"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2018.06.002"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/s140406819"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2036165"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.3414\/ME09\u201002\u20100003"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3284656"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.27892"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2023.105006"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2021.537384"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21020614"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102459"},{"key":"e_1_2_13_16_1","doi-asserted-by":"crossref","unstructured":"Camps J. A.Sam\u00e0 M.Mart\u00edn et\u00a0al.2017. \u201cDeep Learning for Detecting Freezing of Gait Episodes in Parkinson's Disease Based on Accelerometers.\u201d InInternational Work\u2010Conference on Artificial Neural Networks 344\u2013355.Cham:Springer.https:\/\/doi.org\/10.1007\/978\u20103\u2010319\u201059147\u20106_30.","DOI":"10.1007\/978-3-319-59147-6_30"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.10.017"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3278657"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.09.012"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.23159"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474\u20104422(18)30295\u20103"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.0130489"},{"key":"e_1_2_13_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010024\u201018906\u20105"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378\u20104371(02)01744\u20102"},{"key":"e_1_2_13_25_1","doi-asserted-by":"crossref","unstructured":"Hu J. L.Shen andG.Sun.2018. \u201cSqueeze\u2010and\u2010Excitation Networks.\u201d InProceedings of the IEEE conference on computer vision and pattern recognition 7132\u20137141.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105602"},{"key":"e_1_2_13_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2923209"},{"key":"e_1_2_13_28_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00029"},{"key":"e_1_2_13_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2015.249"},{"key":"e_1_2_13_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2020.110776"},{"key":"e_1_2_13_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rehab.2021.101620"},{"key":"e_1_2_13_32_1","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2017.0215"},{"key":"e_1_2_13_33_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010023\u201002731\u20108"},{"key":"e_1_2_13_34_1","doi-asserted-by":"publisher","DOI":"10.1177\/15459683231166934"},{"key":"e_1_2_13_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9111919"},{"key":"e_1_2_13_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22710"},{"key":"e_1_2_13_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2021.08.014"},{"key":"e_1_2_13_38_1","unstructured":"Masiala S.2017.Detection of Freezing of Gait in Patients With Parkinson's Disease Using Deep Recurrent Neural Networks. PhD diss. Tilburg University."},{"key":"e_1_2_13_39_1","doi-asserted-by":"crossref","unstructured":"Mazilu S. U.Blanke andG.Tr\u00f6ster.2015. \u201cGait Wrist and Sensors: Detecting Freezing of Gait in Parkinson's Disease From Wrist Movement.\u201d2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) 579\u2013584.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/PERCOMW.2015.7134102.","DOI":"10.1109\/PERCOMW.2015.7134102"},{"key":"e_1_2_13_40_1","doi-asserted-by":"crossref","unstructured":"Mekruksavanich S. P.Jantawong andA.Jitpattanakul.2022. \u201cComparative Analysis of CNN\u2010Based Deep Learning Approaches on Complex Activity Recognition.\u201d In2022 Joint International Conference on Digital Arts Media and Technology with ECTI Northern Section Conference on Electrical Electronics Computer and Telecommunications Engineering (ECTI DAMT & NCON) 338\u2013341.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/ECTIDAMTNCON53731.2022.9720320.","DOI":"10.1109\/ECTIDAMTNCON53731.2022.9720320"},{"key":"e_1_2_13_41_1","doi-asserted-by":"crossref","unstructured":"Mikos V. C. H.Heng A.Tay et\u00a0al.2017. \u201cReal\u2010Time Patient Adaptivity for Freezing of Gait Classification Through Semi\u2010Supervised Neural Networks.\u201d In2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 871\u2013876.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/ICMLA.2017.00\u201046.","DOI":"10.1109\/ICMLA.2017.00-46"},{"key":"e_1_2_13_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sleep.2023.11.021"},{"key":"e_1_2_13_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2007.08.023"},{"key":"e_1_2_13_44_1","doi-asserted-by":"publisher","DOI":"10.1186\/1743\u20100003\u201010\u201019"},{"key":"e_1_2_13_45_1","doi-asserted-by":"publisher","DOI":"10.2522\/ptj.20090091"},{"key":"e_1_2_13_46_1","doi-asserted-by":"publisher","DOI":"10.3390\/s17112556"},{"key":"e_1_2_13_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2910165"},{"key":"e_1_2_13_48_1","first-page":"14200","article-title":"Attention Bottlenecks for Multimodal Fusion","volume":"34","author":"Nagrani A.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_13_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474\u20104422(15)00041\u20101"},{"key":"e_1_2_13_50_1","doi-asserted-by":"publisher","DOI":"10.3233\/JPD\u2010130282"},{"key":"e_1_2_13_51_1","doi-asserted-by":"crossref","unstructured":"\u00d6nder M. \u00dc.\u015eent\u00fcrk K.Polat andD.Paulraj.2023. \u201cClassification of Freezing of Gait in Parkinson's Disease Using Machine Learning Algorithms.\u201d In2023 International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering (RMKMATE) 1\u20135.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/RMKMATE59243.2023.10368706.","DOI":"10.1109\/RMKMATE59243.2023.10368706"},{"key":"e_1_2_13_52_1","doi-asserted-by":"crossref","unstructured":"Ouyang S. Z.Chen S.Chen andJ.Zhao.2023. \u201cPrediction of Freezing of Gait in Parkinson's Disease Using Time\u2010Series Data From Wearable Sensors.\u201d In2023 42nd Chinese Control Conference (CCC) 3269\u20133273.https:\/\/doi.org\/10.23919\/CCC58697.2023.10241134.","DOI":"10.23919\/CCC58697.2023.10241134"},{"key":"e_1_2_13_53_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21062246"},{"key":"e_1_2_13_54_1","first-page":"3","volume-title":"Computer Vision \u2014 ECCV 2018, Lecture Notes in Computer Science","author":"Park J.","year":"2018"},{"key":"e_1_2_13_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2665438"},{"key":"e_1_2_13_56_1","doi-asserted-by":"crossref","unstructured":"Polat K.2019. \u201cFreezing of Gait (FoG) Detection Using Logistic Regression in Parkinson's Disease From Acceleration Signals.\u201d In2019 Scientific Meeting on Electrical\u2010Electronics & Biomedical Engineering and Computer Science (EBBT) 1\u20134.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/EBBT.2019.8742042.","DOI":"10.1109\/EBBT.2019.8742042"},{"key":"e_1_2_13_57_1","doi-asserted-by":"crossref","unstructured":"Rezaee K. A.Badiei andS.Meshgini.2020. \u201cA Hybrid Deep Transfer Learning Based Approach for COVID\u201019 Classification in Chest X\u2010ray Images.\u201d In2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME) 234\u2013241.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/ICBME51989.2020.9319426.","DOI":"10.1109\/ICBME51989.2020.9319426"},{"key":"e_1_2_13_58_1","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12480"},{"key":"e_1_2_13_59_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16040475"},{"key":"e_1_2_13_60_1","doi-asserted-by":"publisher","DOI":"10.1002\/gps.4414"},{"key":"e_1_2_13_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.05.009"},{"key":"e_1_2_13_62_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8020119"},{"key":"e_1_2_13_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2018.04.032"},{"key":"e_1_2_13_64_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12984\u2010021\u201000958\u20105"},{"key":"e_1_2_13_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2022.3140258"},{"key":"e_1_2_13_66_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20071895"},{"key":"e_1_2_13_67_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415\u2010017\u20108424\u20100"},{"key":"e_1_2_13_68_1","doi-asserted-by":"crossref","unstructured":"Singh N. andP.Tripathi.2023. \u201cGait Assessment Using Optimized Machine Learning and Feature Selection Algorithm for Identifies Parkinson's Disease.\u201d In2023 IEEE International Students' Conference on Electrical Electronics and Computer Science (SCEECS) 1\u20136.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/SCEECS57921.2023.10062964.","DOI":"10.1109\/SCEECS57921.2023.10062964"},{"key":"e_1_2_13_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3277749"},{"key":"e_1_2_13_70_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.22144"},{"key":"e_1_2_13_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2018.8615791"},{"key":"e_1_2_13_72_1","doi-asserted-by":"publisher","DOI":"10.1111\/acel.13031"},{"key":"e_1_2_13_73_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010023\u201039862\u20104"},{"key":"e_1_2_13_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.07.015"},{"key":"e_1_2_13_75_1","unstructured":"Yu F. andV.Koltun.2016. \u201cMulti\u2010Scale Context Aggregation by Dilated Convolutions.\u201d arXiv preprint arXiv:1511.07122. Accessed February 8 2022."},{"key":"e_1_2_13_76_1","doi-asserted-by":"crossref","unstructured":"Yuan A. M. andS.Chakraborty.2020. \u201cA Study of Deep Learning for Predicting Freeze of Gait in Patients With Parkinson's Disease.\u201d In2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)1324\u20131331.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/ICMLA51294.2020.00207.","DOI":"10.1109\/ICMLA51294.2020.00207"},{"key":"e_1_2_13_77_1","doi-asserted-by":"crossref","unstructured":"Zhang Y. andD.Gu.2019. \u201cA Deep Convolutional\u2010recurrent Neural Network for Freezing of Gait Detection in Patients With Parkinson's Disease.\u201d In2019 12th International Congress on Image and Signal Processing BioMedical Engineering and Informatics (CISP\u2010BMEI) 1\u20136.Piscataway:IEEE.https:\/\/doi.org\/10.1109\/CISP\u2010BMEI48845.2019.8965723.","DOI":"10.1109\/CISP-BMEI48845.2019.8965723"}],"container-title":["Brain and Behavior"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/brb3.70206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T08:49:14Z","timestamp":1738140554000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/brb3.70206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":76,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1002\/brb3.70206"],"URL":"https:\/\/doi.org\/10.1002\/brb3.70206","archive":["Portico"],"relation":{},"ISSN":["2162-3279","2162-3279"],"issn-type":[{"value":"2162-3279","type":"print"},{"value":"2162-3279","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]},"assertion":[{"value":"2024-09-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70206"}}