Zeitschriftenartikel zum Thema „Classification of biomedical time series“
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Ramanujam, E., und S. Padmavathi. „Genetic time series motif discovery for time series classification“. International Journal of Biomedical Engineering and Technology 31, Nr. 1 (2019): 47. http://dx.doi.org/10.1504/ijbet.2019.101051.
Der volle Inhalt der QuelleJin, Lin-peng, und Jun Dong. „Ensemble Deep Learning for Biomedical Time Series Classification“. Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6212684.
Der volle Inhalt der QuelleIvaturi, Praharsh, Matteo Gadaleta, Amitabh C. Pandey, Michael Pazzani, Steven R. Steinhubl und Giorgio Quer. „A Comprehensive Explanation Framework for Biomedical Time Series Classification“. IEEE Journal of Biomedical and Health Informatics 25, Nr. 7 (Juli 2021): 2398–408. http://dx.doi.org/10.1109/jbhi.2021.3060997.
Der volle Inhalt der QuelleWang, Jin, Ping Liu, Mary F. H. She, Saeid Nahavandi und Abbas Kouzani. „Bag-of-words representation for biomedical time series classification“. Biomedical Signal Processing and Control 8, Nr. 6 (November 2013): 634–44. http://dx.doi.org/10.1016/j.bspc.2013.06.004.
Der volle Inhalt der QuelleKu-Maldonado, Carlos Alejandro, und Erik Molino-Minero-Re. „Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks“. Revista Mexicana de Ingeniería Biomédica 44, Nr. 4 (17.08.2023): 105–16. http://dx.doi.org/10.17488/rmib.44.4.7.
Der volle Inhalt der QuelleGupta, R., A. Mittal, K. Singh, V. Narang und S. Roy. „Time-series approach to protein classification problem“. IEEE Engineering in Medicine and Biology Magazine 28, Nr. 4 (Juli 2009): 32–37. http://dx.doi.org/10.1109/memb.2009.932903.
Der volle Inhalt der QuelleWang, Will Ke, Ina Chen, Leeor Hershkovich, Jiamu Yang, Ayush Shetty, Geetika Singh, Yihang Jiang et al. „A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications“. Sensors 22, Nr. 20 (20.10.2022): 8016. http://dx.doi.org/10.3390/s22208016.
Der volle Inhalt der QuelleLemus, Mariano, João P. Beirão, Nikola Paunković, Alexandra M. Carvalho und Paulo Mateus. „Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data“. Entropy 22, Nr. 1 (30.12.2019): 49. http://dx.doi.org/10.3390/e22010049.
Der volle Inhalt der QuelleAthavale, Yashodhan, Sridhar Krishnan und Aziz Guergachi. „Pattern Classification of Signals Using Fisher Kernels“. Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/467175.
Der volle Inhalt der QuelleCarreiro, André V., Orlando Anunciação, João A. Carriço und Sara C. Madeira. „Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series“. Journal of Integrative Bioinformatics 8, Nr. 3 (01.12.2011): 73–89. http://dx.doi.org/10.1515/jib-2011-175.
Der volle Inhalt der QuellePiepjohn, Patricia, Christin Bald, Gregor Kuhlenbäumer, Jos Steffen Becktepe, Günther Deuschl und Gerhard Schmidt. „Real-time classification of movement patterns of tremor patients“. Biomedical Engineering / Biomedizinische Technik 67, Nr. 2 (24.02.2022): 119–30. http://dx.doi.org/10.1515/bmt-2021-0140.
Der volle Inhalt der QuelleFulcher, Ben D., Max A. Little und Nick S. Jones. „Highly comparative time-series analysis: the empirical structure of time series and their methods“. Journal of The Royal Society Interface 10, Nr. 83 (06.06.2013): 20130048. http://dx.doi.org/10.1098/rsif.2013.0048.
Der volle Inhalt der QuelleGamidullaeva, Leyla Ayvarovna, und Vsevolod Chernyshenko. „Using Decision-Making Block of Computer-Based Intelligent Biomedical Avatar for Applied Research in Bioinformatics“. International Journal of Applied Research in Bioinformatics 9, Nr. 2 (Juli 2019): 24–34. http://dx.doi.org/10.4018/ijarb.2019070102.
Der volle Inhalt der QuelleAlarcón, Ángel Serrano, Natividad Martínez Madrid, Ralf Seepold und Juan Antonio Ortega Ramirez. „Main requirements of end-to-end deep learning models for biomedical time series classification in healthcare environments“. Procedia Computer Science 207 (2022): 3038–46. http://dx.doi.org/10.1016/j.procs.2022.09.532.
Der volle Inhalt der QuelleCarreiro, André V., Artur J. Ferreira, Mário A. T. Figueiredo und Sara C. Madeira. „Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series“. Journal of Integrative Bioinformatics 9, Nr. 3 (01.12.2012): 105–20. http://dx.doi.org/10.1515/jib-2012-207.
Der volle Inhalt der QuelleZhang, Yinghui, Fengyuan Zhang, Yantong Cui und Ruoci Ning. „CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS“. International Journal of Engineering Technologies and Management Research 5, Nr. 2 (08.02.2020): 181–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.161.
Der volle Inhalt der QuelleLipponen, Jukka A., und Mika P. Tarvainen. „A robust algorithm for heart rate variability time series artefact correction using novel beat classification“. Journal of Medical Engineering & Technology 43, Nr. 3 (03.04.2019): 173–81. http://dx.doi.org/10.1080/03091902.2019.1640306.
Der volle Inhalt der QuelleJackson, Rhydon, Debra Knisley, Cecilia McIntosh und Phillip Pfeiffer. „Predicting Flavonoid UGT Regioselectivity“. Advances in Bioinformatics 2011 (30.06.2011): 1–15. http://dx.doi.org/10.1155/2011/506583.
Der volle Inhalt der QuelleJO, YONG-UN, und DO-CHANG OH. „REAL-TIME HAND GESTURE CLASSIFICATION USING CRNN WITH SCALE AVERAGE WAVELET TRANSFORM“. Journal of Mechanics in Medicine and Biology 20, Nr. 10 (Dezember 2020): 2040028. http://dx.doi.org/10.1142/s021951942040028x.
Der volle Inhalt der QuelleGao, Yongxiang, Zhi Zhao, Yimin Chen, Gehendra Mahara, Jialing Huang, Zhuochen Lin und Jinxin Zhang. „Automatic epileptic seizure classification in multichannel EEG time series with linear discriminant analysis“. Technology and Health Care 28, Nr. 1 (13.01.2020): 23–33. http://dx.doi.org/10.3233/thc-181548.
Der volle Inhalt der QuelleChambon, Stanislas, Mathieu N. Galtier, Pierrick J. Arnal, Gilles Wainrib und Alexandre Gramfort. „A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series“. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, Nr. 4 (April 2018): 758–69. http://dx.doi.org/10.1109/tnsre.2018.2813138.
Der volle Inhalt der QuelleArami, Arash, Antonios Poulakakis-Daktylidis, Yen F. Tai und Etienne Burdet. „Prediction of Gait Freezing in Parkinsonian Patients: A Binary Classification Augmented With Time Series Prediction“. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, Nr. 9 (September 2019): 1909–19. http://dx.doi.org/10.1109/tnsre.2019.2933626.
Der volle Inhalt der QuelleDursun, Gizem, Dunja Bijelić, Neşe Ayşit, Burcu Kurt Vatandaşlar, Lidija Radenović, Abdulkerim Çapar, Bilal Ersen Kerman, Pavle R. Andjus, Andrej Korenić und Ufuk Özkaya. „Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging“. PLOS ONE 18, Nr. 2 (06.02.2023): e0281236. http://dx.doi.org/10.1371/journal.pone.0281236.
Der volle Inhalt der QuelleLiu, Chenxi, Israel Cohen, Rotem Vishinkin und Hossam Haick. „Nanomaterial-Based Sensor Array Signal Processing and Tuberculosis Classification Using Machine Learning“. Journal of Low Power Electronics and Applications 13, Nr. 2 (29.05.2023): 39. http://dx.doi.org/10.3390/jlpea13020039.
Der volle Inhalt der QuelleArunachalam, S. P., S. Kapa, S. K. Mulpuru, P. A. Friedman und E. G. Tolkacheva. „Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis“. Journal of Healthcare Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/8632436.
Der volle Inhalt der QuelleTripathy, R. K., und U. Rajendra Acharya. „Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework“. Biocybernetics and Biomedical Engineering 38, Nr. 4 (2018): 890–902. http://dx.doi.org/10.1016/j.bbe.2018.05.005.
Der volle Inhalt der QuelleCuesta-Frau, David, Juan Pablo Murillo-Escobar, Diana Alexandra Orrego und Edilson Delgado-Trejos. „Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications“. Entropy 21, Nr. 4 (10.04.2019): 385. http://dx.doi.org/10.3390/e21040385.
Der volle Inhalt der QuelleWang, Jialing, Shiwei Cheng, Jieming Tian und Yuefan Gao. „A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification“. Biomedical Signal Processing and Control 83 (Mai 2023): 104627. http://dx.doi.org/10.1016/j.bspc.2023.104627.
Der volle Inhalt der QuelleBAI, G. MERCY, und P. VENKADESH. „TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES“. Journal of Mechanics in Medicine and Biology 21, Nr. 06 (21.06.2021): 2150041. http://dx.doi.org/10.1142/s021951942150041x.
Der volle Inhalt der QuelleChang, Yuan-Hsiang, Kuniya Abe, Hideo Yokota, Kazuhiro Sudo, Yukio Nakamura und Ming-Dar Tsai. „HUMAN INDUCED PLURIPOTENT STEM CELL REGION DETECTION IN BRIGHT-FIELD MICROSCOPY IMAGES USING CONVOLUTIONAL NEURAL NETWORKS“. Biomedical Engineering: Applications, Basis and Communications 31, Nr. 02 (April 2019): 1950009. http://dx.doi.org/10.4015/s1016237219500091.
Der volle Inhalt der QuelleResta, Michele, Anna Monreale und Davide Bacciu. „Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals“. Entropy 23, Nr. 8 (17.08.2021): 1064. http://dx.doi.org/10.3390/e23081064.
Der volle Inhalt der QuelleDissanayake, W. M. N. D., und Maheshi B. Dissanayake. „A Novel LSTM-based Data Synthesis Approach for Performance Improvement in Detecting Epileptic Seizures“. WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 20 (10.10.2023): 132–39. http://dx.doi.org/10.37394/23208.2023.20.13.
Der volle Inhalt der QuelleZhu, Mengyun, Ximin Fan, Weijing Liu, Jianying Shen, Wei Chen, Yawei Xu und Xuejing Yu. „Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison“. Journal of Healthcare Engineering 2021 (06.12.2021): 1–11. http://dx.doi.org/10.1155/2021/1336762.
Der volle Inhalt der QuelleSzigeti, Balázs, Ajinkya Deogade und Barbara Webb. „Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states“. Journal of The Royal Society Interface 12, Nr. 113 (Dezember 2015): 20150899. http://dx.doi.org/10.1098/rsif.2015.0899.
Der volle Inhalt der QuelleChatterjee, Shre Kumar, Saptarshi Das, Koushik Maharatna, Elisa Masi, Luisa Santopolo, Stefano Mancuso und Andrea Vitaletti. „Exploring strategies for classification of external stimuli using statistical features of the plant electrical response“. Journal of The Royal Society Interface 12, Nr. 104 (März 2015): 20141225. http://dx.doi.org/10.1098/rsif.2014.1225.
Der volle Inhalt der QuelleUyulan, Caglar, Türker Tekin Ergüzel und Nevzat Tarhan. „Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification“. Biomedical Engineering / Biomedizinische Technik 64, Nr. 5 (25.09.2019): 529–42. http://dx.doi.org/10.1515/bmt-2018-0105.
Der volle Inhalt der QuelleMakhir, Abdelmalek, My Hachem El Yousfi Alaoui und Larbi Belarbi. „Comprehensive Cardiac Ischemia Classification Using Hybrid CNN-Based Models“. International Journal of Online and Biomedical Engineering (iJOE) 20, Nr. 03 (27.02.2024): 154–65. http://dx.doi.org/10.3991/ijoe.v20i03.45769.
Der volle Inhalt der QuelleCuesta-Frau, David, Jakub Schneider, Eduard Bakštein, Pavel Vostatek, Filip Spaniel und Daniel Novák. „Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study“. Entropy 22, Nr. 11 (01.11.2020): 1243. http://dx.doi.org/10.3390/e22111243.
Der volle Inhalt der QuelleKhorasani, Abed, Mohammad Reza Daliri und Mohammad Pooyan. „Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model“. Biomedical Engineering / Biomedizinische Technik 61, Nr. 1 (01.02.2016): 119–26. http://dx.doi.org/10.1515/bmt-2014-0089.
Der volle Inhalt der QuelleBALOGLU, ULAS BARAN, und ÖZAL YILDIRIM. „CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION“. Journal of Mechanics in Medicine and Biology 19, Nr. 01 (Februar 2019): 1940005. http://dx.doi.org/10.1142/s0219519419400050.
Der volle Inhalt der QuelleBogdanov, M. R., G. R. Shakhmametova und N. N. Oskin. „Possibility of Using the Attention Mechanism in Multimodal Recognition of Cardiovascular Diseases“. Programmnaya Ingeneria 15, Nr. 11 (18.11.2024): 578–88. http://dx.doi.org/10.17587/prin.15.578-588.
Der volle Inhalt der QuelleAmarantidis, Lampros Chrysovalantis, und Daniel Abásolo. „Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy“. Entropy 21, Nr. 9 (27.08.2019): 840. http://dx.doi.org/10.3390/e21090840.
Der volle Inhalt der QuelleZhu, Lingxia, Zhiping Xu und Ting Fang. „Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning“. Journal of Healthcare Engineering 2021 (27.10.2021): 1–8. http://dx.doi.org/10.1155/2021/6050433.
Der volle Inhalt der QuelleJing, Junyuan, Jing Zhang, Aiping Liu, Min Gao, Ruobing Qian und Xun Chen. „ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network“. Journal of Healthcare Engineering 2023 (29.11.2023): 1–10. http://dx.doi.org/10.1155/2023/1755121.
Der volle Inhalt der QuelleHeo, Suncheol, Jae Yong Yu, Eun Ae Kang, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Yebin Chegal et al. „Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach“. Healthcare Informatics Research 29, Nr. 3 (31.07.2023): 246–55. http://dx.doi.org/10.4258/hir.2023.29.3.246.
Der volle Inhalt der QuelleNigat, Tsedenya Debebe, Tilahun Melak Sitote und Berihun Molla Gedefaw. „Fungal Skin Disease Classification Using the Convolutional Neural Network“. Journal of Healthcare Engineering 2023 (30.05.2023): 1–9. http://dx.doi.org/10.1155/2023/6370416.
Der volle Inhalt der QuelleCuesta-Frau, David, Daniel Novák, Vacláv Burda, Daniel Abasolo, Tricia Adjei, Manuel Varela, Borja Vargas et al. „Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods“. Complexity 2019 (14.02.2019): 1–10. http://dx.doi.org/10.1155/2019/6070518.
Der volle Inhalt der QuelleAhammed, Kawser, und Mosabber Uddin Ahmed. „QUANTIFICATION OF MENTAL STRESS USING COMPLEXITY ANALYSIS OF EEG SIGNALS“. Biomedical Engineering: Applications, Basis and Communications 32, Nr. 02 (April 2020): 2050011. http://dx.doi.org/10.4015/s1016237220500118.
Der volle Inhalt der QuelleAlrowais, Fadwa, Faiz Abdullah Alotaibi, Abdulkhaleq Q. A. Hassan, Radwa Marzouk, Mrim M. Alnfiai und Ahmed Sayed. „Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images“. Biomimetics 8, Nr. 7 (10.11.2023): 538. http://dx.doi.org/10.3390/biomimetics8070538.
Der volle Inhalt der QuelleMaheshwari, Saumil, Aman Agarwal, Anupam Shukla und Ritu Tiwari. „A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: patients with cardiovascular disease“. Biomedical Engineering / Biomedizinische Technik 65, Nr. 4 (27.08.2020): 435–46. http://dx.doi.org/10.1515/bmt-2018-0206.
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